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Machine Learning 1 DSBA 2025/2026

2025-08-29T16:46:38Z

Depressingsupremacy: /* Deadline Extensions and Makeup */

More detailed information can be found in the [https://404.com syllabus of the course]

== Course Schedule ==

DSBA Machine Learning 1, ICEF Machine Learning 2025-2026. This syllabus is shared by 2 programs (differences specified where necessary):

* DSBA (ПАД ФКН): TBD
* ICEF (МИЭФ): TBD

== Teachers and Assistants ==

{| class="wikitable" style="text-align:center"
|-
! Role !! DSBA 231 !! DSBA 232 !! DSBA 233 !! DSBA 234 !! ICEF
|-
|| '''Lecturers'''
| colspan="5" | [https://www.hse.ru/en/org/persons/223985242 Alexey Boldyrev], [https://www.hse.ru/en/staff/mekarpov Maksim Karpov]
|-
|| '''Workshop Tutors'''
|| [https://www.linkedin.com/in/kirill-bykov-b81629211/, Kirill Bykov]
|| [https://www.hse.ru/en/staff/sara, Sara Ali]
|| NA
|| [https://www.hse.ru/en/staff/sara, Vsevolod Ovchinnikov]
|| [https://www.hse.ru/en/org/persons/401648437, Majid Sohrabi]
|-
|| '''Teaching Assistants'''
|| Nikolay Dvoryanchikov
|| Georgii Solovyev
|| NA
|| Maria Sudakova
|| Isa Gadaev
|}

== Useful links ==

# [https://edu.hse.ru/course/view.php?id=204727 '''Moodle LMS'''] (a.k.a. Smart LMS): for posting weekly material, additional videos, calendar, and for posting/collecting/grading quizzes, tests, and HW, etc.
# [https://drive.google.com/drive/folders/1IKcKL5JxnW_XPDFXA-DUFiuzCwUzphiB?usp=sharing '''Google Drive''']: for release of seminar Colab notebooks, lecture presentation slides, Starter Colab files for Kaggle competitions.
# [https://colab.research.google.com/ '''Google Colaboratory''']: for individual manual-graded assignments, group Kaggle assignments and reproducible seminar’s notebooks.
* We require the use of LaTeX and Markdown syntax for all write ups.
# [http://Kaggle.com '''Kaggle.com''']: for data science competitions in teams of 1-3 students.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you and credit your effort.

'''Optional Systems (maintained by Teaching Team)'''

# [https://404.com '''Telegram Сhannel''']: for the course announcements (HW, timetable changes, etc.).
# [https://Discourse.hsemlp.ru '''Discourse''']: for all discussions beyond office hours and which are irrelevant in the comments to the Telegram channel announcements.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you.
# [http://DataCamp.com '''DataCamp.com''']: for additional preparatory courses in the first few weeks.

== Course Description ==

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods.

* The first two modules (Sep-Dec`25) DSBA and ICEF students apply Python programming language and popular packages to investigate/visualize datasets and develop machine learning models that solve theoretical and data-driven problems.

# The course aims to help the students to develop an understanding of learning from data, to familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.
# DSBA and ICEF students: the course is designed to prepare DSBA/ICEF students for the upcoming University of London (UoL) examination.

== Grading System ==

Grades:

'''First Semester Grade = 0.15 * Final Test + 0.15 * Hackathon (Exam) + 0.25 * Kaggle competitions + 0.1 * Home assignments + 0.15 * Midterm Test + 0.2 * Quizzes'''

Relevant grading formulas are presented on the official course home page ([https://www.hse.ru/ba/data/courses/1071114714.html, DSBA Machine Learning 1], [https://www.hse.ru/ba/icef/courses/858762359.html, ICEF Machine Learning]). Moodle’s gradebook shows your up to date performance, including your current constituent and aggregate grades. If you suspect an error in grade calculation, please let us know ASAP. We use [https://docs.moodle.org/311/en/Grade_aggregation natural grade aggregation] in LMS. Rounding [https://en.wikipedia.org/wiki/Rounding#Rounding_to_the_nearest_integer to the nearest integer] is used to report 0-10 scale grades to HSE. There are no blocking grading components at the DSBA program, at the ICEF program the Exam in April is blocking.

=== Homework (HW) Assignment ===

* HW is released via Moodle/Smart LMS and is also announced in the Telegram Channel.
* '''Individual''': These are individualized (not in groups!) assignments of two kinds
** Auto-graded assignments (thanks to STACK plugin with Maxima CAS backend). Some written responses will be selectively hand-graded by TAs.
#* Quick Maxima syntax is provided below and in each assignment.

** Manual-graded assignments include analysis of datasets, analytical and conceptual problems, and programming assignments.
#* Submit HW via Moodle/Smart LMS as both shared links to Google Colab and derived PDF.
##* All text explanations must be written in Markdown cells directly in Google Colab notebook.
##* Graders leave feedback in PDF and execute Google Colab to reproduce your results.
* '''Group''': Students are self-assigned into groups to compete on Kaggle.com. Teamwork is evaluated by instructors. Team’s grade is awarded to everyone on a team.

=== Midterm Test and Exam ===

# We will have a cumulative in-class midterm test/exam in the middle and at the end of the semester (during the [https://www.hse.ru/en/studyspravka/grafik/ HSE examination sessions]). Do not book travel tickets that conflict with test dates.
# Moodle/Smart LMS based.
# Tests and exams are individual, i.e. no collaboration. Generative models, web searching, lecture and/or seminar materials, and textbooks are not allowed.
# Test questions are drawn from quiz banks, not HW. HW deepens your understanding, but a test measures it.
# There is a free navigation between questions, i.e. you can move back or forward the test questions.
# Results will be announced after the midterm test/exam within 5 working days.

=== University of London (UoL), Course ST3189 (ML) ===

1. Coursework Project in Python (or R) programming language is for DSBA/ICEF students only and is administered by LSE/UoL. It is released around January and is due around April 1\. Although students are given a 3-4 months window, this exercise is meant to be completed in a few days. Typically, students work on it in Feb/Mar. More details on the [https://emfss.elearning.london.ac.uk/ UoL site].

=== 5-10 minute Quizzes during seminar in LMS \- biweekly ===

# Only students present in the classroom during the seminar are allowed to take the quiz. At the beginning of the seminar the seminar assistant will remove absent student(s) from the group list in moodle settings for the current quiz.
# Quizzes are based on lectures, seminars, textbooks, posted videos, and other material delivered via our course.
# Quizzes individualized (shuffled and sampled from question banks) for each student. Most questions are auto-generated.
# All choice questions are [https://docs.moodle.org/311/en/Multiple_Choice_question_type#Multiple-answer_questions, multiple-choice] (regardless of singular/plural formulation). Incorrect answers lower your score to prevent guessing.
# Numeric answers are typically accepted with 0.01=1% of error, i.e. round to at least 4 decimal places, if needed. Please do not round any intermediate calculations. It’s best to use as many decimals as fits in the answer box.
# The quiz answers are released after all groups write the quiz .
# We always use [https://en.wikipedia.org/wiki/Natural_logarithm, natural logarithm] (inverse of exp()) in this course.

=== Deadline Extensions and Makeup ===

# Only valid verifiable excuses are accepted for 1-2 day extensions.
#* DSBA and ICEF students: submit your doctor's note via [https://www.hse.ru/ba/data/onlineservices, student services].
#* Contact the [https://discourse.hsemlp.ru/new-message?groupname=Instructors, instructors] in discourse regarding your illness in order to verify it via study office
# If you miss a deadline (with a valid/verifiable excuse), contact instructors ASAP in a private post to arrange a new deadline.
# Submissions are not allowed after the solutions have been released.
# Any test/exam retakes will be rescheduled as per university policy (see also dedicated rubric below)
# Note: accommodating exceptions is difficult and time consuming. Typically, a verifiable medical emergency is a valid reason, but travel and conferences are not. It is the student's responsibility to start their work early, so as to hedge against any unforeseeable life event.

Machine Learning 1 DSBA 2025/2026

2025-08-29T16:46:26Z

Depressingsupremacy: /* Deadline Extensions and Makeup */

More detailed information can be found in the [https://404.com syllabus of the course]

== Course Schedule ==

DSBA Machine Learning 1, ICEF Machine Learning 2025-2026. This syllabus is shared by 2 programs (differences specified where necessary):

* DSBA (ПАД ФКН): TBD
* ICEF (МИЭФ): TBD

== Teachers and Assistants ==

{| class="wikitable" style="text-align:center"
|-
! Role !! DSBA 231 !! DSBA 232 !! DSBA 233 !! DSBA 234 !! ICEF
|-
|| '''Lecturers'''
| colspan="5" | [https://www.hse.ru/en/org/persons/223985242 Alexey Boldyrev], [https://www.hse.ru/en/staff/mekarpov Maksim Karpov]
|-
|| '''Workshop Tutors'''
|| [https://www.linkedin.com/in/kirill-bykov-b81629211/, Kirill Bykov]
|| [https://www.hse.ru/en/staff/sara, Sara Ali]
|| NA
|| [https://www.hse.ru/en/staff/sara, Vsevolod Ovchinnikov]
|| [https://www.hse.ru/en/org/persons/401648437, Majid Sohrabi]
|-
|| '''Teaching Assistants'''
|| Nikolay Dvoryanchikov
|| Georgii Solovyev
|| NA
|| Maria Sudakova
|| Isa Gadaev
|}

== Useful links ==

# [https://edu.hse.ru/course/view.php?id=204727 '''Moodle LMS'''] (a.k.a. Smart LMS): for posting weekly material, additional videos, calendar, and for posting/collecting/grading quizzes, tests, and HW, etc.
# [https://drive.google.com/drive/folders/1IKcKL5JxnW_XPDFXA-DUFiuzCwUzphiB?usp=sharing '''Google Drive''']: for release of seminar Colab notebooks, lecture presentation slides, Starter Colab files for Kaggle competitions.
# [https://colab.research.google.com/ '''Google Colaboratory''']: for individual manual-graded assignments, group Kaggle assignments and reproducible seminar’s notebooks.
* We require the use of LaTeX and Markdown syntax for all write ups.
# [http://Kaggle.com '''Kaggle.com''']: for data science competitions in teams of 1-3 students.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you and credit your effort.

'''Optional Systems (maintained by Teaching Team)'''

# [https://404.com '''Telegram Сhannel''']: for the course announcements (HW, timetable changes, etc.).
# [https://Discourse.hsemlp.ru '''Discourse''']: for all discussions beyond office hours and which are irrelevant in the comments to the Telegram channel announcements.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you.
# [http://DataCamp.com '''DataCamp.com''']: for additional preparatory courses in the first few weeks.

== Course Description ==

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods.

* The first two modules (Sep-Dec`25) DSBA and ICEF students apply Python programming language and popular packages to investigate/visualize datasets and develop machine learning models that solve theoretical and data-driven problems.

# The course aims to help the students to develop an understanding of learning from data, to familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.
# DSBA and ICEF students: the course is designed to prepare DSBA/ICEF students for the upcoming University of London (UoL) examination.

== Grading System ==

Grades:

'''First Semester Grade = 0.15 * Final Test + 0.15 * Hackathon (Exam) + 0.25 * Kaggle competitions + 0.1 * Home assignments + 0.15 * Midterm Test + 0.2 * Quizzes'''

Relevant grading formulas are presented on the official course home page ([https://www.hse.ru/ba/data/courses/1071114714.html, DSBA Machine Learning 1], [https://www.hse.ru/ba/icef/courses/858762359.html, ICEF Machine Learning]). Moodle’s gradebook shows your up to date performance, including your current constituent and aggregate grades. If you suspect an error in grade calculation, please let us know ASAP. We use [https://docs.moodle.org/311/en/Grade_aggregation natural grade aggregation] in LMS. Rounding [https://en.wikipedia.org/wiki/Rounding#Rounding_to_the_nearest_integer to the nearest integer] is used to report 0-10 scale grades to HSE. There are no blocking grading components at the DSBA program, at the ICEF program the Exam in April is blocking.

=== Homework (HW) Assignment ===

* HW is released via Moodle/Smart LMS and is also announced in the Telegram Channel.
* '''Individual''': These are individualized (not in groups!) assignments of two kinds
** Auto-graded assignments (thanks to STACK plugin with Maxima CAS backend). Some written responses will be selectively hand-graded by TAs.
#* Quick Maxima syntax is provided below and in each assignment.

** Manual-graded assignments include analysis of datasets, analytical and conceptual problems, and programming assignments.
#* Submit HW via Moodle/Smart LMS as both shared links to Google Colab and derived PDF.
##* All text explanations must be written in Markdown cells directly in Google Colab notebook.
##* Graders leave feedback in PDF and execute Google Colab to reproduce your results.
* '''Group''': Students are self-assigned into groups to compete on Kaggle.com. Teamwork is evaluated by instructors. Team’s grade is awarded to everyone on a team.

=== Midterm Test and Exam ===

# We will have a cumulative in-class midterm test/exam in the middle and at the end of the semester (during the [https://www.hse.ru/en/studyspravka/grafik/ HSE examination sessions]). Do not book travel tickets that conflict with test dates.
# Moodle/Smart LMS based.
# Tests and exams are individual, i.e. no collaboration. Generative models, web searching, lecture and/or seminar materials, and textbooks are not allowed.
# Test questions are drawn from quiz banks, not HW. HW deepens your understanding, but a test measures it.
# There is a free navigation between questions, i.e. you can move back or forward the test questions.
# Results will be announced after the midterm test/exam within 5 working days.

=== University of London (UoL), Course ST3189 (ML) ===

1. Coursework Project in Python (or R) programming language is for DSBA/ICEF students only and is administered by LSE/UoL. It is released around January and is due around April 1\. Although students are given a 3-4 months window, this exercise is meant to be completed in a few days. Typically, students work on it in Feb/Mar. More details on the [https://emfss.elearning.london.ac.uk/ UoL site].

=== 5-10 minute Quizzes during seminar in LMS \- biweekly ===

# Only students present in the classroom during the seminar are allowed to take the quiz. At the beginning of the seminar the seminar assistant will remove absent student(s) from the group list in moodle settings for the current quiz.
# Quizzes are based on lectures, seminars, textbooks, posted videos, and other material delivered via our course.
# Quizzes individualized (shuffled and sampled from question banks) for each student. Most questions are auto-generated.
# All choice questions are [https://docs.moodle.org/311/en/Multiple_Choice_question_type#Multiple-answer_questions, multiple-choice] (regardless of singular/plural formulation). Incorrect answers lower your score to prevent guessing.
# Numeric answers are typically accepted with 0.01=1% of error, i.e. round to at least 4 decimal places, if needed. Please do not round any intermediate calculations. It’s best to use as many decimals as fits in the answer box.
# The quiz answers are released after all groups write the quiz .
# We always use [https://en.wikipedia.org/wiki/Natural_logarithm, natural logarithm] (inverse of exp()) in this course.

=== Deadline Extensions and Makeup ===

# Only valid verifiable excuses are accepted for 1-2 day extensions.
#* DSBA and ICEF students: submit your doctor's note via [https://www.hse.ru/ba/data/onlineservices, student services].
#* Contact the [https://discourse.hsemlp.ru/new-message?groupname=Instructors, Instructors] in discourse regarding your illness in order to verify it via study office
# If you miss a deadline (with a valid/verifiable excuse), contact instructors ASAP in a private post to arrange a new deadline.
# Submissions are not allowed after the solutions have been released.
# Any test/exam retakes will be rescheduled as per university policy (see also dedicated rubric below)
# Note: accommodating exceptions is difficult and time consuming. Typically, a verifiable medical emergency is a valid reason, but travel and conferences are not. It is the student's responsibility to start their work early, so as to hedge against any unforeseeable life event.

Machine Learning 1 DSBA 2025/2026

2025-08-29T16:45:33Z

Depressingsupremacy: /* 5-10 minute Quizzes during seminar in LMS \- biweekly */

More detailed information can be found in the [https://404.com syllabus of the course]

== Course Schedule ==

DSBA Machine Learning 1, ICEF Machine Learning 2025-2026. This syllabus is shared by 2 programs (differences specified where necessary):

* DSBA (ПАД ФКН): TBD
* ICEF (МИЭФ): TBD

== Teachers and Assistants ==

{| class="wikitable" style="text-align:center"
|-
! Role !! DSBA 231 !! DSBA 232 !! DSBA 233 !! DSBA 234 !! ICEF
|-
|| '''Lecturers'''
| colspan="5" | [https://www.hse.ru/en/org/persons/223985242 Alexey Boldyrev], [https://www.hse.ru/en/staff/mekarpov Maksim Karpov]
|-
|| '''Workshop Tutors'''
|| [https://www.linkedin.com/in/kirill-bykov-b81629211/, Kirill Bykov]
|| [https://www.hse.ru/en/staff/sara, Sara Ali]
|| NA
|| [https://www.hse.ru/en/staff/sara, Vsevolod Ovchinnikov]
|| [https://www.hse.ru/en/org/persons/401648437, Majid Sohrabi]
|-
|| '''Teaching Assistants'''
|| Nikolay Dvoryanchikov
|| Georgii Solovyev
|| NA
|| Maria Sudakova
|| Isa Gadaev
|}

== Useful links ==

# [https://edu.hse.ru/course/view.php?id=204727 '''Moodle LMS'''] (a.k.a. Smart LMS): for posting weekly material, additional videos, calendar, and for posting/collecting/grading quizzes, tests, and HW, etc.
# [https://drive.google.com/drive/folders/1IKcKL5JxnW_XPDFXA-DUFiuzCwUzphiB?usp=sharing '''Google Drive''']: for release of seminar Colab notebooks, lecture presentation slides, Starter Colab files for Kaggle competitions.
# [https://colab.research.google.com/ '''Google Colaboratory''']: for individual manual-graded assignments, group Kaggle assignments and reproducible seminar’s notebooks.
* We require the use of LaTeX and Markdown syntax for all write ups.
# [http://Kaggle.com '''Kaggle.com''']: for data science competitions in teams of 1-3 students.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you and credit your effort.

'''Optional Systems (maintained by Teaching Team)'''

# [https://404.com '''Telegram Сhannel''']: for the course announcements (HW, timetable changes, etc.).
# [https://Discourse.hsemlp.ru '''Discourse''']: for all discussions beyond office hours and which are irrelevant in the comments to the Telegram channel announcements.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you.
# [http://DataCamp.com '''DataCamp.com''']: for additional preparatory courses in the first few weeks.

== Course Description ==

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods.

* The first two modules (Sep-Dec`25) DSBA and ICEF students apply Python programming language and popular packages to investigate/visualize datasets and develop machine learning models that solve theoretical and data-driven problems.

# The course aims to help the students to develop an understanding of learning from data, to familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.
# DSBA and ICEF students: the course is designed to prepare DSBA/ICEF students for the upcoming University of London (UoL) examination.

== Grading System ==

Grades:

'''First Semester Grade = 0.15 * Final Test + 0.15 * Hackathon (Exam) + 0.25 * Kaggle competitions + 0.1 * Home assignments + 0.15 * Midterm Test + 0.2 * Quizzes'''

Relevant grading formulas are presented on the official course home page ([https://www.hse.ru/ba/data/courses/1071114714.html, DSBA Machine Learning 1], [https://www.hse.ru/ba/icef/courses/858762359.html, ICEF Machine Learning]). Moodle’s gradebook shows your up to date performance, including your current constituent and aggregate grades. If you suspect an error in grade calculation, please let us know ASAP. We use [https://docs.moodle.org/311/en/Grade_aggregation natural grade aggregation] in LMS. Rounding [https://en.wikipedia.org/wiki/Rounding#Rounding_to_the_nearest_integer to the nearest integer] is used to report 0-10 scale grades to HSE. There are no blocking grading components at the DSBA program, at the ICEF program the Exam in April is blocking.

=== Homework (HW) Assignment ===

* HW is released via Moodle/Smart LMS and is also announced in the Telegram Channel.
* '''Individual''': These are individualized (not in groups!) assignments of two kinds
** Auto-graded assignments (thanks to STACK plugin with Maxima CAS backend). Some written responses will be selectively hand-graded by TAs.
#* Quick Maxima syntax is provided below and in each assignment.

** Manual-graded assignments include analysis of datasets, analytical and conceptual problems, and programming assignments.
#* Submit HW via Moodle/Smart LMS as both shared links to Google Colab and derived PDF.
##* All text explanations must be written in Markdown cells directly in Google Colab notebook.
##* Graders leave feedback in PDF and execute Google Colab to reproduce your results.
* '''Group''': Students are self-assigned into groups to compete on Kaggle.com. Teamwork is evaluated by instructors. Team’s grade is awarded to everyone on a team.

=== Midterm Test and Exam ===

# We will have a cumulative in-class midterm test/exam in the middle and at the end of the semester (during the [https://www.hse.ru/en/studyspravka/grafik/ HSE examination sessions]). Do not book travel tickets that conflict with test dates.
# Moodle/Smart LMS based.
# Tests and exams are individual, i.e. no collaboration. Generative models, web searching, lecture and/or seminar materials, and textbooks are not allowed.
# Test questions are drawn from quiz banks, not HW. HW deepens your understanding, but a test measures it.
# There is a free navigation between questions, i.e. you can move back or forward the test questions.
# Results will be announced after the midterm test/exam within 5 working days.

=== University of London (UoL), Course ST3189 (ML) ===

1. Coursework Project in Python (or R) programming language is for DSBA/ICEF students only and is administered by LSE/UoL. It is released around January and is due around April 1\. Although students are given a 3-4 months window, this exercise is meant to be completed in a few days. Typically, students work on it in Feb/Mar. More details on the [https://emfss.elearning.london.ac.uk/ UoL site].

=== 5-10 minute Quizzes during seminar in LMS \- biweekly ===

# Only students present in the classroom during the seminar are allowed to take the quiz. At the beginning of the seminar the seminar assistant will remove absent student(s) from the group list in moodle settings for the current quiz.
# Quizzes are based on lectures, seminars, textbooks, posted videos, and other material delivered via our course.
# Quizzes individualized (shuffled and sampled from question banks) for each student. Most questions are auto-generated.
# All choice questions are [https://docs.moodle.org/311/en/Multiple_Choice_question_type#Multiple-answer_questions, multiple-choice] (regardless of singular/plural formulation). Incorrect answers lower your score to prevent guessing.
# Numeric answers are typically accepted with 0.01=1% of error, i.e. round to at least 4 decimal places, if needed. Please do not round any intermediate calculations. It’s best to use as many decimals as fits in the answer box.
# The quiz answers are released after all groups write the quiz .
# We always use [https://en.wikipedia.org/wiki/Natural_logarithm, natural logarithm] (inverse of exp()) in this course.

=== Deadline Extensions and Makeup ===

# Only valid verifiable excuses are accepted for 1-2 day extensions.
#* DSBA and ICEF students: submit your doctor's note via [student services](https://www.hse.ru/ba/data/onlineservices).
#* Write to [Instructors](https://discourse.hsemlp.ru/new-message?groupname=Instructors) a message in discourse about your illness in order to verify it via study office
# If you miss a deadline (with a valid/verifiable excuse), contact instructors ASAP in a private post to arrange a new deadline.
# Submissions are not allowed after the solutions have been released.
# Any test/exam retakes will be rescheduled as per university policy (see also dedicated rubric below)
# Note: accommodating exceptions is difficult and time consuming. Typically, a verifiable medical emergency is a valid reason, but travel and conferences are not. It is the student's responsibility to start their work early, so as to hedge against any unforeseeable life event.

Machine Learning 1 DSBA 2025/2026

2025-08-29T16:44:47Z

Depressingsupremacy: /* Useful links */

More detailed information can be found in the [https://404.com syllabus of the course]

== Course Schedule ==

DSBA Machine Learning 1, ICEF Machine Learning 2025-2026. This syllabus is shared by 2 programs (differences specified where necessary):

* DSBA (ПАД ФКН): TBD
* ICEF (МИЭФ): TBD

== Teachers and Assistants ==

{| class="wikitable" style="text-align:center"
|-
! Role !! DSBA 231 !! DSBA 232 !! DSBA 233 !! DSBA 234 !! ICEF
|-
|| '''Lecturers'''
| colspan="5" | [https://www.hse.ru/en/org/persons/223985242 Alexey Boldyrev], [https://www.hse.ru/en/staff/mekarpov Maksim Karpov]
|-
|| '''Workshop Tutors'''
|| [https://www.linkedin.com/in/kirill-bykov-b81629211/, Kirill Bykov]
|| [https://www.hse.ru/en/staff/sara, Sara Ali]
|| NA
|| [https://www.hse.ru/en/staff/sara, Vsevolod Ovchinnikov]
|| [https://www.hse.ru/en/org/persons/401648437, Majid Sohrabi]
|-
|| '''Teaching Assistants'''
|| Nikolay Dvoryanchikov
|| Georgii Solovyev
|| NA
|| Maria Sudakova
|| Isa Gadaev
|}

== Useful links ==

# [https://edu.hse.ru/course/view.php?id=204727 '''Moodle LMS'''] (a.k.a. Smart LMS): for posting weekly material, additional videos, calendar, and for posting/collecting/grading quizzes, tests, and HW, etc.
# [https://drive.google.com/drive/folders/1IKcKL5JxnW_XPDFXA-DUFiuzCwUzphiB?usp=sharing '''Google Drive''']: for release of seminar Colab notebooks, lecture presentation slides, Starter Colab files for Kaggle competitions.
# [https://colab.research.google.com/ '''Google Colaboratory''']: for individual manual-graded assignments, group Kaggle assignments and reproducible seminar’s notebooks.
* We require the use of LaTeX and Markdown syntax for all write ups.
# [http://Kaggle.com '''Kaggle.com''']: for data science competitions in teams of 1-3 students.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you and credit your effort.

'''Optional Systems (maintained by Teaching Team)'''

# [https://404.com '''Telegram Сhannel''']: for the course announcements (HW, timetable changes, etc.).
# [https://Discourse.hsemlp.ru '''Discourse''']: for all discussions beyond office hours and which are irrelevant in the comments to the Telegram channel announcements.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you.
# [http://DataCamp.com '''DataCamp.com''']: for additional preparatory courses in the first few weeks.

== Course Description ==

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods.

* The first two modules (Sep-Dec`25) DSBA and ICEF students apply Python programming language and popular packages to investigate/visualize datasets and develop machine learning models that solve theoretical and data-driven problems.

# The course aims to help the students to develop an understanding of learning from data, to familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.
# DSBA and ICEF students: the course is designed to prepare DSBA/ICEF students for the upcoming University of London (UoL) examination.

== Grading System ==

Grades:

'''First Semester Grade = 0.15 * Final Test + 0.15 * Hackathon (Exam) + 0.25 * Kaggle competitions + 0.1 * Home assignments + 0.15 * Midterm Test + 0.2 * Quizzes'''

Relevant grading formulas are presented on the official course home page ([https://www.hse.ru/ba/data/courses/1071114714.html, DSBA Machine Learning 1], [https://www.hse.ru/ba/icef/courses/858762359.html, ICEF Machine Learning]). Moodle’s gradebook shows your up to date performance, including your current constituent and aggregate grades. If you suspect an error in grade calculation, please let us know ASAP. We use [https://docs.moodle.org/311/en/Grade_aggregation natural grade aggregation] in LMS. Rounding [https://en.wikipedia.org/wiki/Rounding#Rounding_to_the_nearest_integer to the nearest integer] is used to report 0-10 scale grades to HSE. There are no blocking grading components at the DSBA program, at the ICEF program the Exam in April is blocking.

=== Homework (HW) Assignment ===

* HW is released via Moodle/Smart LMS and is also announced in the Telegram Channel.
* '''Individual''': These are individualized (not in groups!) assignments of two kinds
** Auto-graded assignments (thanks to STACK plugin with Maxima CAS backend). Some written responses will be selectively hand-graded by TAs.
#* Quick Maxima syntax is provided below and in each assignment.

** Manual-graded assignments include analysis of datasets, analytical and conceptual problems, and programming assignments.
#* Submit HW via Moodle/Smart LMS as both shared links to Google Colab and derived PDF.
##* All text explanations must be written in Markdown cells directly in Google Colab notebook.
##* Graders leave feedback in PDF and execute Google Colab to reproduce your results.
* '''Group''': Students are self-assigned into groups to compete on Kaggle.com. Teamwork is evaluated by instructors. Team’s grade is awarded to everyone on a team.

=== Midterm Test and Exam ===

# We will have a cumulative in-class midterm test/exam in the middle and at the end of the semester (during the [https://www.hse.ru/en/studyspravka/grafik/ HSE examination sessions]). Do not book travel tickets that conflict with test dates.
# Moodle/Smart LMS based.
# Tests and exams are individual, i.e. no collaboration. Generative models, web searching, lecture and/or seminar materials, and textbooks are not allowed.
# Test questions are drawn from quiz banks, not HW. HW deepens your understanding, but a test measures it.
# There is a free navigation between questions, i.e. you can move back or forward the test questions.
# Results will be announced after the midterm test/exam within 5 working days.

=== University of London (UoL), Course ST3189 (ML) ===

1. Coursework Project in Python (or R) programming language is for DSBA/ICEF students only and is administered by LSE/UoL. It is released around January and is due around April 1\. Although students are given a 3-4 months window, this exercise is meant to be completed in a few days. Typically, students work on it in Feb/Mar. More details on the [https://emfss.elearning.london.ac.uk/ UoL site].

=== 5-10 minute Quizzes during seminar in LMS \- biweekly ===

# Only students present in the classroom during the seminar are allowed to take the quiz. At the beginning of the seminar the seminar assistant will remove absent student(s) from the group list in moodle settings for the current quiz.
# Quizzes are based on lectures, seminars, textbooks, posted videos, and other material delivered via our course.
# Quizzes individualized (shuffled and sampled from question banks) for each student. Most questions are auto-generated.
# All choice questions are [multiple-choice](https://docs.moodle.org/311/en/Multiple_Choice_question_type#Multiple-answer_questions) (regardless of singular/plural formulation). Incorrect answers lower your score to prevent guessing.
# Numeric answers are typically accepted with 0.01=1% of error, i.e. round to at least 4 decimal places, if needed. Please do not round any intermediate calculations. It’s best to use as many decimals as fits in the answer box.
# The quiz answers are released after all groups write the quiz .
# We always use [natural logarithm](https://en.wikipedia.org/wiki/Natural_logarithm) (inverse of exp()) in this course.

=== Deadline Extensions and Makeup ===

# Only valid verifiable excuses are accepted for 1-2 day extensions.
#* DSBA and ICEF students: submit your doctor's note via [student services](https://www.hse.ru/ba/data/onlineservices).
#* Write to [Instructors](https://discourse.hsemlp.ru/new-message?groupname=Instructors) a message in discourse about your illness in order to verify it via study office
# If you miss a deadline (with a valid/verifiable excuse), contact instructors ASAP in a private post to arrange a new deadline.
# Submissions are not allowed after the solutions have been released.
# Any test/exam retakes will be rescheduled as per university policy (see also dedicated rubric below)
# Note: accommodating exceptions is difficult and time consuming. Typically, a verifiable medical emergency is a valid reason, but travel and conferences are not. It is the student's responsibility to start their work early, so as to hedge against any unforeseeable life event.

Machine Learning 1 DSBA 2025/2026

2025-08-29T16:44:20Z

Depressingsupremacy:

More detailed information can be found in the [https://404.com syllabus of the course]

== Course Schedule ==

DSBA Machine Learning 1, ICEF Machine Learning 2025-2026. This syllabus is shared by 2 programs (differences specified where necessary):

* DSBA (ПАД ФКН): TBD
* ICEF (МИЭФ): TBD

== Teachers and Assistants ==

{| class="wikitable" style="text-align:center"
|-
! Role !! DSBA 231 !! DSBA 232 !! DSBA 233 !! DSBA 234 !! ICEF
|-
|| '''Lecturers'''
| colspan="5" | [https://www.hse.ru/en/org/persons/223985242 Alexey Boldyrev], [https://www.hse.ru/en/staff/mekarpov Maksim Karpov]
|-
|| '''Workshop Tutors'''
|| [https://www.linkedin.com/in/kirill-bykov-b81629211/, Kirill Bykov]
|| [https://www.hse.ru/en/staff/sara, Sara Ali]
|| NA
|| [https://www.hse.ru/en/staff/sara, Vsevolod Ovchinnikov]
|| [https://www.hse.ru/en/org/persons/401648437, Majid Sohrabi]
|-
|| '''Teaching Assistants'''
|| Nikolay Dvoryanchikov
|| Georgii Solovyev
|| NA
|| Maria Sudakova
|| Isa Gadaev
|}

== Useful links ==

# [https://edu.hse.ru/course/view.php?id=204727 '''Moodle LMS'''] (a.k.a. Smart LMS): for posting weekly material, additional videos, calendar, and for posting/collecting/grading quizzes, tests, and HW, etc.
# [https://drive.google.com/drive/folders/1IKcKL5JxnW_XPDFXA-DUFiuzCwUzphiB?usp=sharing '''Google Drive''']: for release of seminar Colab notebooks, lecture presentation slides, Starter Colab files for Kaggle competitions.
# [https://colab.research.google.com/ '''Google Colaboratory''']: for individual manual-graded assignments, group Kaggle assignments and reproducible seminar’s notebooks.
* We require the use of LaTeX and Markdown syntax for all write ups.
# [http://Kaggle.com '''Kaggle.com''']: for data science competitions in teams of 1-3 students.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you and credit your effort.

'''Optional Systems (yet maintained by Teaching Team)'''

# [https://404.com '''Telegram Сhannel''']: for the course announcements (HW, timetable changes, etc.).
# [https://Discourse.hsemlp.ru '''Discourse''']: for all discussions beyond office hours and which are irrelevant in the comments to the Telegram channel announcements.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you.
# [http://DataCamp.com '''DataCamp.com''']: for additional preparatory courses in the first few weeks.

== Course Description ==

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods.

* The first two modules (Sep-Dec`25) DSBA and ICEF students apply Python programming language and popular packages to investigate/visualize datasets and develop machine learning models that solve theoretical and data-driven problems.

# The course aims to help the students to develop an understanding of learning from data, to familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.
# DSBA and ICEF students: the course is designed to prepare DSBA/ICEF students for the upcoming University of London (UoL) examination.

== Grading System ==

Grades:

'''First Semester Grade = 0.15 * Final Test + 0.15 * Hackathon (Exam) + 0.25 * Kaggle competitions + 0.1 * Home assignments + 0.15 * Midterm Test + 0.2 * Quizzes'''

Relevant grading formulas are presented on the official course home page ([https://www.hse.ru/ba/data/courses/1071114714.html, DSBA Machine Learning 1], [https://www.hse.ru/ba/icef/courses/858762359.html, ICEF Machine Learning]). Moodle’s gradebook shows your up to date performance, including your current constituent and aggregate grades. If you suspect an error in grade calculation, please let us know ASAP. We use [https://docs.moodle.org/311/en/Grade_aggregation natural grade aggregation] in LMS. Rounding [https://en.wikipedia.org/wiki/Rounding#Rounding_to_the_nearest_integer to the nearest integer] is used to report 0-10 scale grades to HSE. There are no blocking grading components at the DSBA program, at the ICEF program the Exam in April is blocking.

=== Homework (HW) Assignment ===

* HW is released via Moodle/Smart LMS and is also announced in the Telegram Channel.
* '''Individual''': These are individualized (not in groups!) assignments of two kinds
** Auto-graded assignments (thanks to STACK plugin with Maxima CAS backend). Some written responses will be selectively hand-graded by TAs.
#* Quick Maxima syntax is provided below and in each assignment.

** Manual-graded assignments include analysis of datasets, analytical and conceptual problems, and programming assignments.
#* Submit HW via Moodle/Smart LMS as both shared links to Google Colab and derived PDF.
##* All text explanations must be written in Markdown cells directly in Google Colab notebook.
##* Graders leave feedback in PDF and execute Google Colab to reproduce your results.
* '''Group''': Students are self-assigned into groups to compete on Kaggle.com. Teamwork is evaluated by instructors. Team’s grade is awarded to everyone on a team.

=== Midterm Test and Exam ===

# We will have a cumulative in-class midterm test/exam in the middle and at the end of the semester (during the [https://www.hse.ru/en/studyspravka/grafik/ HSE examination sessions]). Do not book travel tickets that conflict with test dates.
# Moodle/Smart LMS based.
# Tests and exams are individual, i.e. no collaboration. Generative models, web searching, lecture and/or seminar materials, and textbooks are not allowed.
# Test questions are drawn from quiz banks, not HW. HW deepens your understanding, but a test measures it.
# There is a free navigation between questions, i.e. you can move back or forward the test questions.
# Results will be announced after the midterm test/exam within 5 working days.

=== University of London (UoL), Course ST3189 (ML) ===

1. Coursework Project in Python (or R) programming language is for DSBA/ICEF students only and is administered by LSE/UoL. It is released around January and is due around April 1\. Although students are given a 3-4 months window, this exercise is meant to be completed in a few days. Typically, students work on it in Feb/Mar. More details on the [https://emfss.elearning.london.ac.uk/ UoL site].

=== 5-10 minute Quizzes during seminar in LMS \- biweekly ===

# Only students present in the classroom during the seminar are allowed to take the quiz. At the beginning of the seminar the seminar assistant will remove absent student(s) from the group list in moodle settings for the current quiz.
# Quizzes are based on lectures, seminars, textbooks, posted videos, and other material delivered via our course.
# Quizzes individualized (shuffled and sampled from question banks) for each student. Most questions are auto-generated.
# All choice questions are [multiple-choice](https://docs.moodle.org/311/en/Multiple_Choice_question_type#Multiple-answer_questions) (regardless of singular/plural formulation). Incorrect answers lower your score to prevent guessing.
# Numeric answers are typically accepted with 0.01=1% of error, i.e. round to at least 4 decimal places, if needed. Please do not round any intermediate calculations. It’s best to use as many decimals as fits in the answer box.
# The quiz answers are released after all groups write the quiz .
# We always use [natural logarithm](https://en.wikipedia.org/wiki/Natural_logarithm) (inverse of exp()) in this course.

=== Deadline Extensions and Makeup ===

# Only valid verifiable excuses are accepted for 1-2 day extensions.
#* DSBA and ICEF students: submit your doctor's note via [student services](https://www.hse.ru/ba/data/onlineservices).
#* Write to [Instructors](https://discourse.hsemlp.ru/new-message?groupname=Instructors) a message in discourse about your illness in order to verify it via study office
# If you miss a deadline (with a valid/verifiable excuse), contact instructors ASAP in a private post to arrange a new deadline.
# Submissions are not allowed after the solutions have been released.
# Any test/exam retakes will be rescheduled as per university policy (see also dedicated rubric below)
# Note: accommodating exceptions is difficult and time consuming. Typically, a verifiable medical emergency is a valid reason, but travel and conferences are not. It is the student's responsibility to start their work early, so as to hedge against any unforeseeable life event.

Machine Learning 1 DSBA 2025/2026

2025-08-29T16:43:39Z

Depressingsupremacy: Предварительная версия

More detailed information can be found in [https://404.com Syllabus for HSE Machine Learning course]

== Course Schedule ==

DSBA Machine Learning 1, ICEF Machine Learning 2025-2026. This syllabus is shared by 2 programs (differences specified where necessary):

* DSBA (ПАД ФКН): TBD
* ICEF (МИЭФ): TBD

== Teachers and Assistants ==

{| class="wikitable" style="text-align:center"
|-
! Role !! DSBA 231 !! DSBA 232 !! DSBA 233 !! DSBA 234 !! ICEF
|-
|| '''Lecturers'''
| colspan="5" | [https://www.hse.ru/en/org/persons/223985242 Alexey Boldyrev], [https://www.hse.ru/en/staff/mekarpov Maksim Karpov]
|-
|| '''Workshop Tutors'''
|| [https://www.linkedin.com/in/kirill-bykov-b81629211/, Kirill Bykov]
|| [https://www.hse.ru/en/staff/sara, Sara Ali]
|| NA
|| [https://www.hse.ru/en/staff/sara, Vsevolod Ovchinnikov]
|| [https://www.hse.ru/en/org/persons/401648437, Majid Sohrabi]
|-
|| '''Teaching Assistants'''
|| Nikolay Dvoryanchikov
|| Georgii Solovyev
|| NA
|| Maria Sudakova
|| Isa Gadaev
|}

== Useful links ==

# [https://edu.hse.ru/course/view.php?id=204727 '''Moodle LMS'''] (a.k.a. Smart LMS): for posting weekly material, additional videos, calendar, and for posting/collecting/grading quizzes, tests, and HW, etc.
# [https://drive.google.com/drive/folders/1IKcKL5JxnW_XPDFXA-DUFiuzCwUzphiB?usp=sharing '''Google Drive''']: for release of seminar Colab notebooks, lecture presentation slides, Starter Colab files for Kaggle competitions.
# [https://colab.research.google.com/ '''Google Colaboratory''']: for individual manual-graded assignments, group Kaggle assignments and reproducible seminar’s notebooks.
* We require the use of LaTeX and Markdown syntax for all write ups.
# [http://Kaggle.com '''Kaggle.com''']: for data science competitions in teams of 1-3 students.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you and credit your effort.

'''Optional Systems (yet maintained by Teaching Team)'''

# [https://404.com '''Telegram Сhannel''']: for the course announcements (HW, timetable changes, etc.).
# [https://Discourse.hsemlp.ru '''Discourse''']: for all discussions beyond office hours and which are irrelevant in the comments to the Telegram channel announcements.
* Ensure that your name & email matches exactly to those in Moodle LMS or we can lose you in grade matching. Update the profile with your presentable photo to help us authenticate you.
# [http://DataCamp.com '''DataCamp.com''']: for additional preparatory courses in the first few weeks.

== Course Description ==

This course introduces the students to the elements of machine learning, including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods.

* The first two modules (Sep-Dec`25) DSBA and ICEF students apply Python programming language and popular packages to investigate/visualize datasets and develop machine learning models that solve theoretical and data-driven problems.

# The course aims to help the students to develop an understanding of learning from data, to familiarize them with a wide variety of algorithmic and model based methods to extract information from data, teach to apply and evaluate suitable methods to various datasets by model selection and predictive performance evaluation.
# DSBA and ICEF students: the course is designed to prepare DSBA/ICEF students for the upcoming University of London (UoL) examination.

== Grading System ==

Grades:

'''First Semester Grade = 0.15 * Final Test + 0.15 * Hackathon (Exam) + 0.25 * Kaggle competitions + 0.1 * Home assignments + 0.15 * Midterm Test + 0.2 * Quizzes'''

Relevant grading formulas are presented on the official course home page ([https://www.hse.ru/ba/data/courses/1071114714.html, DSBA Machine Learning 1], [https://www.hse.ru/ba/icef/courses/858762359.html, ICEF Machine Learning]). Moodle’s gradebook shows your up to date performance, including your current constituent and aggregate grades. If you suspect an error in grade calculation, please let us know ASAP. We use [https://docs.moodle.org/311/en/Grade_aggregation natural grade aggregation] in LMS. Rounding [https://en.wikipedia.org/wiki/Rounding#Rounding_to_the_nearest_integer to the nearest integer] is used to report 0-10 scale grades to HSE. There are no blocking grading components at the DSBA program, at the ICEF program the Exam in April is blocking.

=== Homework (HW) Assignment ===

* HW is released via Moodle/Smart LMS and is also announced in the Telegram Channel.
* '''Individual''': These are individualized (not in groups!) assignments of two kinds
** Auto-graded assignments (thanks to STACK plugin with Maxima CAS backend). Some written responses will be selectively hand-graded by TAs.
#* Quick Maxima syntax is provided below and in each assignment.

** Manual-graded assignments include analysis of datasets, analytical and conceptual problems, and programming assignments.
#* Submit HW via Moodle/Smart LMS as both shared links to Google Colab and derived PDF.
##* All text explanations must be written in Markdown cells directly in Google Colab notebook.
##* Graders leave feedback in PDF and execute Google Colab to reproduce your results.
* '''Group''': Students are self-assigned into groups to compete on Kaggle.com. Teamwork is evaluated by instructors. Team’s grade is awarded to everyone on a team.

=== Midterm Test and Exam ===

# We will have a cumulative in-class midterm test/exam in the middle and at the end of the semester (during the [https://www.hse.ru/en/studyspravka/grafik/ HSE examination sessions]). Do not book travel tickets that conflict with test dates.
# Moodle/Smart LMS based.
# Tests and exams are individual, i.e. no collaboration. Generative models, web searching, lecture and/or seminar materials, and textbooks are not allowed.
# Test questions are drawn from quiz banks, not HW. HW deepens your understanding, but a test measures it.
# There is a free navigation between questions, i.e. you can move back or forward the test questions.
# Results will be announced after the midterm test/exam within 5 working days.

=== University of London (UoL), Course ST3189 (ML) ===

1. Coursework Project in Python (or R) programming language is for DSBA/ICEF students only and is administered by LSE/UoL. It is released around January and is due around April 1\. Although students are given a 3-4 months window, this exercise is meant to be completed in a few days. Typically, students work on it in Feb/Mar. More details on the [https://emfss.elearning.london.ac.uk/ UoL site].

=== 5-10 minute Quizzes during seminar in LMS \- biweekly ===

# Only students present in the classroom during the seminar are allowed to take the quiz. At the beginning of the seminar the seminar assistant will remove absent student(s) from the group list in moodle settings for the current quiz.
# Quizzes are based on lectures, seminars, textbooks, posted videos, and other material delivered via our course.
# Quizzes individualized (shuffled and sampled from question banks) for each student. Most questions are auto-generated.
# All choice questions are [multiple-choice](https://docs.moodle.org/311/en/Multiple_Choice_question_type#Multiple-answer_questions) (regardless of singular/plural formulation). Incorrect answers lower your score to prevent guessing.
# Numeric answers are typically accepted with 0.01=1% of error, i.e. round to at least 4 decimal places, if needed. Please do not round any intermediate calculations. It’s best to use as many decimals as fits in the answer box.
# The quiz answers are released after all groups write the quiz .
# We always use [natural logarithm](https://en.wikipedia.org/wiki/Natural_logarithm) (inverse of exp()) in this course.

=== Deadline Extensions and Makeup ===

# Only valid verifiable excuses are accepted for 1-2 day extensions.
#* DSBA and ICEF students: submit your doctor's note via [student services](https://www.hse.ru/ba/data/onlineservices).
#* Write to [Instructors](https://discourse.hsemlp.ru/new-message?groupname=Instructors) a message in discourse about your illness in order to verify it via study office
# If you miss a deadline (with a valid/verifiable excuse), contact instructors ASAP in a private post to arrange a new deadline.
# Submissions are not allowed after the solutions have been released.
# Any test/exam retakes will be rescheduled as per university policy (see also dedicated rubric below)
# Note: accommodating exceptions is difficult and time consuming. Typically, a verifiable medical emergency is a valid reason, but travel and conferences are not. It is the student's responsibility to start their work early, so as to hedge against any unforeseeable life event.

Machine learning 1 DSBA 2023/2024

2023-10-01T16:34:38Z

Depressingsupremacy: Первая версия страницы курса

== Course description ==

This course introduces the students to the elements of machine learning (ML), including supervised and unsupervised methods such as linear and logistic regressions, splines, decision trees, support vector machines, bootstrapping, random forests, boosting, regularized methods, etc. The course covers classic ML. The weekly or biweekly team-based Kaggle competitions are released in Python programming language. Other assignments (quizzes and theoretical derivations) are highly individualized and autograded with tools in Moodle LMS. Participation at lectures, seminars and a class forum is assessed and graded. Pre-requisites: calculus 1, vector calculus, linear algebra, probability/statistics, computer programming in a high level language such as Python. This course is separated into two differing elective tracks, namely, '''Introduction to Statistical Learning'''(ISL) and '''Fundamentals of Statistical Learning'''(FSL). ISL offers a more practical (hands-on) approach, while FSL is more focused on the theoretical aspect of the course' materials.

== General information ==

'''Lectures'''

{| class="wikitable"
|-
| '''ISL''' || [https://www.hse.ru/en/org/persons/223985242 Alexey Boldyrev]
|-
| '''FSL''' || [https://www.hse.ru/en/org/persons/190892428 Maxim Karpov]
|-
|}

'''Seminars'''
{| class="wikitable"
|-
! Group !! ISL group 1 !! ISL group 2 !! FSL
|-
| '''Professor''' || [https://www.hse.ru/org/persons/401667013 Tatiana Yazykova] || [https://www.hse.ru/staff/kvbykov Kirill Bykov] || [https://www.hse.ru/staff/ramazyant Tigran Ramazyan]
|-
| Seminar date/time (room) || Sat 14:40 (D207) || Thu 09:30(D207) || Tue 13:00(R608)
|}

'''Teaching assistants(Both tracks)'''
* Tim Sluev
* Isa Gadaev
* Adam Alkhanashvili
* Daria Lapko
* Grigorii Chebotarev
* Petr Belonovskii
* Elizaveta Kurlovich

== Grading formula ==
'''2023/2024 2nd module''' 
0.3 * Home assignments + 0.2 * Quizzes + 0.2 * Test + 0.1 * Participation + 0.2 * Exam
 
Moodle’s gradebook shows your up to date performance, including your current constituent and aggregate grades. If you suspect an error in grade calculation, please let us know ASAP. We use natural grade aggregation in LMS. Rounding (to the nearest integer) is used to report 0-10 scale grades to HSE. There are no blocking grading components.

== Important links ==
* [https://www.hse.ru/ba/data/courses/841092655.html ISL course page on the HSE website]
* [https://www.hse.ru/ba/data/courses/841087771.html ISL course page on the HSE website]
* [https://docs.google.com/document/d/1gCDuI8C0labz81zv2jOhokd-xszIG8xY/ Syllabus]
* [https://edu.hse.ru/course/view.php?id=133239 Moodle/SmartLMS page]
* [https://discourse.hsemlp.ru HSE MLP Discourse] (used to communicate with the Teaching Team)

English DSBA 2022/2023

2023-05-14T23:35:22Z

Depressingsupremacy:

=== [https://docs.google.com/document/d/1DboxBk3jfOJagGAgjHVGxXdFCrWaDDhB/edit?usp=sharing Syllabus] ===

=== Teachers and Teaching Assistants ===

{| class="wikitable" style="text-align:center"
| Teachers || Anna Ovchinnikova aovchinnikova@gmail.com || Maria Сoen msokol@bk.ru || Marina Sildimirova aprvip1979@mail.ru
|-
| Groups || [https://docs.google.com/spreadsheets/d/1vziMDDUXNDvg-dY7yArnlJulRwpB9bQb25nvyufJ_fI/edit?usp=sharing 221-1] [https://docs.google.com/spreadsheets/d/1Hx5Nka-mqL8UZ-xXXRctrL27X7GYZRSd3N3MWNY8MGw/edit?usp=sharing 223-1] [https://docs.google.com/spreadsheets/d/1Og2BOIqipNjzuMejUuqye4VkOWv-0bN0qRBsEftRtsI/edit?usp=sharing 224-1] || [https://docs.google.com/spreadsheets/d/1P7CxMmYy0xe6Jo0zadefUncS1UsXDzY7RkZvKUaHShY/edit?usp=sharing 221-2] [https://docs.google.com/spreadsheets/d/1xVR9ppUaMrrFFBpD9m9kvEzEwypOwP9BrD4Vb4C2GJg/edit?usp=sharing 222-2] || [https://docs.google.com/spreadsheets/d/18ZJTYJ6dyLJZL-qih8GIQ6-Qs4cJl-iNukwBko77fyc/edit?usp=sharing 222-1] [https://docs.google.com/spreadsheets/d/10A5ZZ1veckBCyRtfywi9D7ZpNSZp_TVFsfYNJ6VMjPo/edit?usp=sharing 223-2] [https://docs.google.com/spreadsheets/d/1Iq0Kh8QQBb2mSPppSx-Dcn6HOQ6skSRhgL3-Bn6EMwU/edit?usp=sharing 224-2]
|}

{| class="wikitable" style="text-align:center"
| Assistants || Isa Gadaev imgadaev@edu.hse.ru || Daria Lapko dalapko@edu.hse.ru || Natalia Berlina npberlina@edu.hse.ru || Iskander Sergazin Isergazin@edu.hse.ru
|-
| Groups || 223-1 224-1 || 221-2 222-2 || 223-2 224-2 || 221-1 222-1
|}

=== Grading system ===

===== 1-2 modules =====
G 1-2 modules = G cumulative 1-2 modules * 0.6 + G exam 2 module* 0.4 
G cumulative 1-2 modules = G attendance, classwork * 0,4 + G homework * 0,3+ G tests * 0,3

===== 3-4 modules =====

G 3-4 modules = G cumulative 3-4 modules * 0.6 + G mock exam * 0.4 
G cumulative 3-4 modules = G attendance, classwork * 0,4 + G homework * 0,3 + G tests * 0,3

===== Final Grade =====

G final = 0.7 * the lowest mark + 0.3 * the highest mark

''where the lowest and the highest marks are taken from G 1-2 modules and G 3-4 modules.''

The arithmetic rounding rule is applied, i.e. 0.1-0.4 is rounded down and 0.5 and higher is rounded up.

===== Bonus points =====

Students can improve their G tests by 1 point. To do so they have to complete up to 10 bonus point tasks in modules 1-2 and up to 10 bonus point tasks in modules 3-4.

=== December 22, English Exam, 09.00-19.00 ===

{| class="wikitable"
| '''09.00-09.20''' || '''R407''' Leave your bags and all smart technology with our TAs
|-
| '''09.20''' || Be seated in '''R503'''
|-
| '''09.30''' || Listening ''(2 listening sections, 20 minutes + time to transfer answers to the answer sheets)''
'''No latecomers will be allowed in the exam room during this or any other part, only in between the test parts'''
|-
| '''10.00''' || Reading ''(2 reading passages, 40 minutes, no extra time to transfer answers)''
|-
| '''10.45''' || Writing ''(20 minutes)''
|-
| '''11.15''' || Leave the exam room
|-
| '''12.00-19.00''' Ovchinnikova A.V., S224 Sildimirova M.A., R506 Coen M.M., N508 || Speaking Interview times are available on the link https://docs.google.com/spreadsheets/d/1aOQNYNYlX-Z_Qb5-xDo0wCWUiCHAxvWE2DMyTiyXGpE/edit?usp=share_link
|-
|}

{| class="wikitable"
|+ Online exam (Kurman Karina)
| '''09.20''' || Join zoom conference https://us06web.zoom.us/j/83338523333?pwd=Y0xFTnF1Y29TWVdKRUlYVFJLSCtpdz09 Код доступа: 000253
|-
| '''09.30''' || Listening
|-
| '''09.55''' || Reading
|-
| '''10.35''' || Writing
|-
| '''11.00''' || Finish the written part of the exam
|-
| '''12.00-12.10''' || Speaking
|}

===== Instructions =====

'''Before the written part''' 
1) The test starts at 09:30, but you should '''be seated at 09.20''', so arrive on time. 
2) Your overcoats must be left downstairs in the cloakroom. 
3) All belongings including your mobile phones are to be left in room '''R407'''. 
4) You can take with you a bottle of water (label-free), a pen, a pencil and an eraser only. 
5) Smartphones, smartwatches and other smarts are not allowed. In case any gadget is found with you, the test results are cancelled and you are disqualified. 

'''During the test''' 
6) You can make notes in the booklets. 
7) Note that '''only your answer sheets''' (for READING and LISTENING) '''are to be checked'''. 
8) Test timing will be noted on the board (for WRITING and READING). 
9) In case you have a question, raise your hand and an assistant/teacher will help you asap. 
10) You cannot talk during the test with other students or look at their answers or booklets. If we catch you doing so, the test results are cancelled and you are disqualified. 
11) You should stop writing when you are asked to finish the test and in case you continue writing, it is regarded as cheating and the test results are cancelled. 

'''Procedure''' 
The test starts with LISTENING. 
The next part to be done is READING. 
The WRITING part comes after the other two. 

'''Speaking''' 
The day finishes with SPEAKING. 
During the interview, no gadgets are allowed. 
You should come 20 minutes before your time. 
You should not discuss the questions you were asked with each other. 

'''Rules for online students'''
* The cameras have to switched on at all times.
* While doing the reading and writing tasks, the sound has to be switched on.
* The exam has to be done individually by each student.
* It is forbidden to talk to anyone during the exam tasks. Talking to someone else is considered cheating and the student is gets a 0 for the exam.
* The writing will be checked for plagiarism using a plagiarism-detection tool.
* When doing any of the tasks, it is forbidden to stand up or to step away from the computer.
* It is forbidden to use one’s phone.
* If you have questions, ask them before the start of each new part or send a private message to the conference host.
* It is also forbidden to eat. One can drink water in a clear glass or a bottle without a label.
* It is forbidden to use dictionaries, to open any tabs in the browser (including Yandex, Google etc) other than the test tab and the google drive tab with the reading texts.
* One can revisit completed tasks and make changes.
* One can use paper and pen to take notes, write down key words and make essay plans while doing the Listening, Reading and Writing tasks.

===== December exam, feedback sessions =====

# Coen M.M.: January 11 (Wednesday), 16.20, D508 (speaking, writing);
# Ovchinnikova A.V.: January 11 (Wednesday), 18.10, D508 (speaking, writing, all reading and listening answer sheets);
# Sildimirova M.A.: January 12, 17.40, D509 (speaking, writing).

=== Mock exam in April ===

[https://docs.google.com/spreadsheets/d/1VNnfWGHtdHxqyEHGI4AMJMVeJO1foEqBFTiFLojagNQ/edit?usp=share_link The schedule for interviews]

The written part of the mock test will be held on '''Saturday from 16.00 until 19.00'''. Please, find more information about it below. The information about the rooms and links is available in the table with your interview times.

{| class="wikitable"
| '''15.45-15.55''' || M302 Leave your bags and all smart technology with our TAs
|-
| '''16.00''' || Be seated in R404
|-
| '''16.05''' || Listening No latecomers will be allowed in the exam room during this or any other part, only in between the test parts
|-
| '''16.45''' || Reading
|-
| '''17.45''' || Writing
|-
| '''18.50''' || Leave the exam room
|-
|}

{| class="wikitable"
|+ Online exam (Kurman K., Potekhin D.)
| '''12.30-12.45''' || Interview: Kurman K. Zoom link: https://us02web.zoom.us/j/87018348684?pwd=L25ldll0eU9PaGpiWG4yVjB6a3EwZz09 password: 280339
|-
| '''11:00-11:15''' || Interview: Potekhin D. Zoom link: https://us02web.zoom.us/j/87018348684?pwd=L25ldll0eU9PaGpiWG4yVjB6a3EwZz09 password: 280339
|-
| '''16.00''' || Join zoom conference https://us06web.zoom.us/j/85963726052?pwd=c3dLbC81YXNIeUorSmlpcTFKb2RmUT09
|-
| '''16.05''' || Listening
|-
| '''16.45''' || Reading
|-
| '''17.45''' || Writing
|}

===== Instructions =====

'''Before the written part''' 
1) The test starts at 16.05, please come to R404 at 15.50. We have to finish at 18.00, so we won't be waiting for late-comers. If you are late, we won't let you in the exam room during the listening part, only after it finishes and before the reading test begins. 
2) All belongings including your mobile phones are to be left in room '''M302'''. 
3) You can take with you a bottle of water (label-free), a pen, a pencil and an eraser only. 
4) Smartphones, smartwatches and other smarts are not allowed. In case any gadget is found with you, the test results are cancelled and you are disqualified. 
 
'''During the test''' 
1) You can make notes in the booklets. 
2) Note that '''only your answer sheets''' (for READING and LISTENING) '''are to be checked.''' 
3) Test timing will be noted on the board (for WRITING and READING). 
4) In case you have a question, raise your hand and an assistant/teacher will help you asap. 
5) You cannot talk during the test with other students or look at their answers or booklets. If we catch you doing so, the test results are cancelled and you are disqualified. 
6) You should stop writing when you are asked to finish the test and in case you continue writing, it is regarded as cheating and the test results are cancelled. 
 
'''Procedure''' 
The test starts with LISTENING. 
The next part to be done is READING. 
The WRITING part comes after the other two. 
 
'''Speaking''' 
During the interview, no gadgets are allowed. 
You should come 20 minutes before your time. 
You should not discuss the questions you were asked with each other. 

===== Feedback sessions =====
'''''Monday, May 15'''''

09.30-10.30 Koen M.M.

link: https://us02web.zoom.us/j/87018348684?pwd=L25ldll0eU9PaGpiWG4yVjB6a3EwZz09

password: 280339

'''''Tuesday, May 16'''''

17.45-18.45 Ovchinnikova A.V., Sildimirova M.A. R407

===== Mock test resit =====

'''''Thursday, May 18'''''

11.00-14.00 Written part, R308

14.30-15.45 Interviews, R205

English DSBA 2022/2023

2023-04-22T07:41:05Z

Depressingsupremacy:

English DSBA 2022/2023

2023-04-22T02:59:36Z

Depressingsupremacy:

=== [https://docs.google.com/document/d/1DboxBk3jfOJagGAgjHVGxXdFCrWaDDhB/edit?usp=sharing Syllabus] ===

=== Teachers and Teaching Assistants ===

{| class="wikitable" style="text-align:center"
| Teachers || Anna Ovchinnikova aovchinnikova@gmail.com || Maria Сoen msokol@bk.ru || Marina Sildimirova aprvip1979@mail.ru
|-
| Groups || [https://docs.google.com/spreadsheets/d/1vziMDDUXNDvg-dY7yArnlJulRwpB9bQb25nvyufJ_fI/edit?usp=sharing 221-1] [https://docs.google.com/spreadsheets/d/1Hx5Nka-mqL8UZ-xXXRctrL27X7GYZRSd3N3MWNY8MGw/edit?usp=sharing 223-1] [https://docs.google.com/spreadsheets/d/1Og2BOIqipNjzuMejUuqye4VkOWv-0bN0qRBsEftRtsI/edit?usp=sharing 224-1] || [https://docs.google.com/spreadsheets/d/1P7CxMmYy0xe6Jo0zadefUncS1UsXDzY7RkZvKUaHShY/edit?usp=sharing 221-2] [https://docs.google.com/spreadsheets/d/1xVR9ppUaMrrFFBpD9m9kvEzEwypOwP9BrD4Vb4C2GJg/edit?usp=sharing 222-2] || [https://docs.google.com/spreadsheets/d/18ZJTYJ6dyLJZL-qih8GIQ6-Qs4cJl-iNukwBko77fyc/edit?usp=sharing 222-1] [https://docs.google.com/spreadsheets/d/10A5ZZ1veckBCyRtfywi9D7ZpNSZp_TVFsfYNJ6VMjPo/edit?usp=sharing 223-2] [https://docs.google.com/spreadsheets/d/1Iq0Kh8QQBb2mSPppSx-Dcn6HOQ6skSRhgL3-Bn6EMwU/edit?usp=sharing 224-2]
|}

{| class="wikitable" style="text-align:center"
| Assistants || Isa Gadaev imgadaev@edu.hse.ru || Daria Lapko dalapko@edu.hse.ru || Natalia Berlina npberlina@edu.hse.ru || Iskander Sergazin Isergazin@edu.hse.ru
|-
| Groups || 223-1 224-1 || 221-2 222-2 || 223-2 224-2 || 221-1 222-1
|}

=== Grading system ===

===== 1-2 modules =====
G 1-2 modules = G cumulative 1-2 modules * 0.6 + G exam 2 module* 0.4 
G cumulative 1-2 modules = G attendance, classwork * 0,4 + G homework * 0,3+ G tests * 0,3

===== 3-4 modules =====

G 3-4 modules = G cumulative 3-4 modules * 0.6 + G mock exam * 0.4 
G cumulative 3-4 modules = G attendance, classwork * 0,4 + G homework * 0,3 + G tests * 0,3

===== Final Grade =====

G final = 0.7 * the lowest mark + 0.3 * the highest mark

''where the lowest and the highest marks are taken from G 1-2 modules and G 3-4 modules.''

The arithmetic rounding rule is applied, i.e. 0.1-0.4 is rounded down and 0.5 and higher is rounded up.

===== Bonus points =====

Students can improve their G tests by 1 point. To do so they have to complete up to 10 bonus point tasks in modules 1-2 and up to 10 bonus point tasks in modules 3-4.

=== December 22, English Exam, 09.00-19.00 ===

{| class="wikitable"
| '''09.00-09.20''' || '''R407''' Leave your bags and all smart technology with our TAs
|-
| '''09.20''' || Be seated in '''R503'''
|-
| '''09.30''' || Listening ''(2 listening sections, 20 minutes + time to transfer answers to the answer sheets)''
'''No latecomers will be allowed in the exam room during this or any other part, only in between the test parts'''
|-
| '''10.00''' || Reading ''(2 reading passages, 40 minutes, no extra time to transfer answers)''
|-
| '''10.45''' || Writing ''(20 minutes)''
|-
| '''11.15''' || Leave the exam room
|-
| '''12.00-19.00''' Ovchinnikova A.V., S224 Sildimirova M.A., R506 Coen M.M., N508 || Speaking Interview times are available on the link https://docs.google.com/spreadsheets/d/1aOQNYNYlX-Z_Qb5-xDo0wCWUiCHAxvWE2DMyTiyXGpE/edit?usp=share_link
|-
|}

{| class="wikitable"
|+ Online exam (Kurman Karina)
| '''09.20''' || Join zoom conference https://us06web.zoom.us/j/83338523333?pwd=Y0xFTnF1Y29TWVdKRUlYVFJLSCtpdz09 Код доступа: 000253
|-
| '''09.30''' || Listening
|-
| '''09.55''' || Reading
|-
| '''10.35''' || Writing
|-
| '''11.00''' || Finish the written part of the exam
|-
| '''12.00-12.10''' || Speaking
|}

===== Instructions =====

'''Before the written part''' 
1) The test starts at 09:30, but you should '''be seated at 09.20''', so arrive on time. 
2) Your overcoats must be left downstairs in the cloakroom. 
3) All belongings including your mobile phones are to be left in room '''R407'''. 
4) You can take with you a bottle of water (label-free), a pen, a pencil and an eraser only. 
5) Smartphones, smartwatches and other smarts are not allowed. In case any gadget is found with you, the test results are cancelled and you are disqualified. 

'''During the test''' 
6) You can make notes in the booklets. 
7) Note that '''only your answer sheets''' (for READING and LISTENING) '''are to be checked'''. 
8) Test timing will be noted on the board (for WRITING and READING). 
9) In case you have a question, raise your hand and an assistant/teacher will help you asap. 
10) You cannot talk during the test with other students or look at their answers or booklets. If we catch you doing so, the test results are cancelled and you are disqualified. 
11) You should stop writing when you are asked to finish the test and in case you continue writing, it is regarded as cheating and the test results are cancelled. 

'''Procedure''' 
The test starts with LISTENING. 
The next part to be done is READING. 
The WRITING part comes after the other two. 

'''Speaking''' 
The day finishes with SPEAKING. 
During the interview, no gadgets are allowed. 
You should come 20 minutes before your time. 
You should not discuss the questions you were asked with each other. 

'''Rules for online students'''
* The cameras have to switched on at all times.
* While doing the reading and writing tasks, the sound has to be switched on.
* The exam has to be done individually by each student.
* It is forbidden to talk to anyone during the exam tasks. Talking to someone else is considered cheating and the student is gets a 0 for the exam.
* The writing will be checked for plagiarism using a plagiarism-detection tool.
* When doing any of the tasks, it is forbidden to stand up or to step away from the computer.
* It is forbidden to use one’s phone.
* If you have questions, ask them before the start of each new part or send a private message to the conference host.
* It is also forbidden to eat. One can drink water in a clear glass or a bottle without a label.
* It is forbidden to use dictionaries, to open any tabs in the browser (including Yandex, Google etc) other than the test tab and the google drive tab with the reading texts.
* One can revisit completed tasks and make changes.
* One can use paper and pen to take notes, write down key words and make essay plans while doing the Listening, Reading and Writing tasks.

===== December exam, feedback sessions =====

# Coen M.M.: January 11 (Wednesday), 16.20, D508 (speaking, writing);
# Ovchinnikova A.V.: January 11 (Wednesday), 18.10, D508 (speaking, writing, all reading and listening answer sheets);
# Sildimirova M.A.: January 12, 17.40, D509 (speaking, writing).

=== Mock exam in April ===

[https://docs.google.com/spreadsheets/d/1VNnfWGHtdHxqyEHGI4AMJMVeJO1foEqBFTiFLojagNQ/edit?usp=share_link The schedule for interviews]

The written part of the mock test will be held on '''Saturday from 16.00 until 19.00'''. Please, find more information about it below. The information about the rooms and links is available in the table with your interview times.

{| class="wikitable"
| '''15.45-15.55''' || M302 Leave your bags and all smart technology with our TAs
|-
| '''16.00''' || Be seated in R404
|-
| '''16.05''' || Listening No latecomers will be allowed in the exam room during this or any other part, only in between the test parts
|-
| '''16.45''' || Reading
|-
| '''17.45''' || Writing
|-
| '''18.50''' || Leave the exam room
|-
|}

{| class="wikitable"
|+ Online exam (Kurman K., Potekhin D.)
| '''12.30-12.45''' || Interview: Kurman K. Zoom link: https://us02web.zoom.us/j/87018348684?pwd=L25ldll0eU9PaGpiWG4yVjB6a3EwZz09 password: 280339
|-
| '''11:00-11:15''' || Interview: Potekhin D. Zoom link: password: 280339
|-
| '''16.00''' || Join zoom conference https://us06web.zoom.us/j/85963726052?pwd=c3dLbC81YXNIeUorSmlpcTFKb2RmUT09
|-
| '''16.05''' || Listening
|-
| '''16.45''' || Reading
|-
| '''17.45''' || Writing
|}

===== Instructions =====

'''Before the written part''' 
1) The test starts at 16.05, please come to R404 at 15.50. We have to finish at 18.00, so we won't be waiting for late-comers. If you are late, we won't let you in the exam room during the listening part, only after it finishes and before the reading test begins. 
2) All belongings including your mobile phones are to be left in room '''M302'''. 
3) You can take with you a bottle of water (label-free), a pen, a pencil and an eraser only. 
4) Smartphones, smartwatches and other smarts are not allowed. In case any gadget is found with you, the test results are cancelled and you are disqualified. 
 
'''During the test''' 
1) You can make notes in the booklets. 
2) Note that '''only your answer sheets''' (for READING and LISTENING) '''are to be checked.''' 
3) Test timing will be noted on the board (for WRITING and READING). 
4) In case you have a question, raise your hand and an assistant/teacher will help you asap. 
5) You cannot talk during the test with other students or look at their answers or booklets. If we catch you doing so, the test results are cancelled and you are disqualified. 
6) You should stop writing when you are asked to finish the test and in case you continue writing, it is regarded as cheating and the test results are cancelled. 
 
'''Procedure''' 
The test starts with LISTENING. 
The next part to be done is READING. 
The WRITING part comes after the other two. 
 
'''Speaking''' 
During the interview, no gadgets are allowed. 
You should come 20 minutes before your time. 
You should not discuss the questions you were asked with each other.

English DSBA 2022/2023

2023-04-19T12:11:54Z

Depressingsupremacy:

English DSBA 2022/2023

2023-01-11T00:25:41Z

Depressingsupremacy:

English DSBA 2022/2023

2022-12-20T01:10:45Z

Depressingsupremacy: Добавлена информация об экзамене во втором модуле.

English DSBA 2022/2023

2022-09-29T08:40:12Z

Depressingsupremacy: /* Syllabus */

English DSBA 2022/2023

2022-09-15T16:16:53Z

Depressingsupremacy: Внёс коррективы по запросу преподавателя

=== [https://docs.google.com/document/d/1TSP_WN02ko4_C19TV9Umpox1TX-_S8ky/edit?usp=sharing Syllabus] ===

=== Teachers and Teaching Assistants ===

{| class="wikitable" style="text-align:center"
| Teachers || Anna Ovchinnikova aovchinnikova@gmail.com || Maria Сoen msokol@bk.ru || Marina Sildimirova aprvip1979@mail.ru
|-
| Groups || [https://docs.google.com/spreadsheets/d/1vziMDDUXNDvg-dY7yArnlJulRwpB9bQb25nvyufJ_fI/edit?usp=sharing 221-1] [https://docs.google.com/spreadsheets/d/1Hx5Nka-mqL8UZ-xXXRctrL27X7GYZRSd3N3MWNY8MGw/edit?usp=sharing 223-1] [https://docs.google.com/spreadsheets/d/1Og2BOIqipNjzuMejUuqye4VkOWv-0bN0qRBsEftRtsI/edit?usp=sharing 224-1] || [https://docs.google.com/spreadsheets/d/1P7CxMmYy0xe6Jo0zadefUncS1UsXDzY7RkZvKUaHShY/edit?usp=sharing 221-2] [https://docs.google.com/spreadsheets/d/1xVR9ppUaMrrFFBpD9m9kvEzEwypOwP9BrD4Vb4C2GJg/edit?usp=sharing 222-2] || [https://docs.google.com/spreadsheets/d/18ZJTYJ6dyLJZL-qih8GIQ6-Qs4cJl-iNukwBko77fyc/edit?usp=sharing 222-1] [https://docs.google.com/spreadsheets/d/10A5ZZ1veckBCyRtfywi9D7ZpNSZp_TVFsfYNJ6VMjPo/edit?usp=sharing 223-2] [https://docs.google.com/spreadsheets/d/1Iq0Kh8QQBb2mSPppSx-Dcn6HOQ6skSRhgL3-Bn6EMwU/edit?usp=sharing 224-2]
|}

{| class="wikitable" style="text-align:center"
| Assistants || Isa Gadaev imgadaev@edu.hse.ru || Daria Lapko dalapko@edu.hse.ru || Natalia Berlina npberlina@edu.hse.ru || Iskander Sergazin Isergazin@edu.hse.ru
|-
| Groups || 223-1 224-1 || 221-2 222-2 || 223-2 224-2 || 221-1 222-1
|}

=== Grading system ===

===== 1-2 modules =====
G 1-2 modules = G cumulative 1-2 modules * 0.6 + G exam 2 module* 0.4 
G cumulative 1-2 modules = G attendance, classwork * 0,4 + G homework * 0,3+ G tests * 0,3

===== 3-4 modules =====

G 3-4 modules = G cumulative 3-4 modules * 0.6 + G mock exam * 0.4 
G cumulative 3-4 modules = G attendance, classwork * 0,4 + G homework * 0,3 + G tests * 0,3

===== Final Grade =====

G final = 0.7 * the lowest mark + 0.3 * the highest mark

''where the lowest and the highest marks are taken from G 1-2 modules and G 3-4 modules.''

The arithmetic rounding rule is applied, i.e. 0.1-0.4 is rounded down and 0.5 and higher is rounded up.

===== Bonus points =====

Students can improve their G tests by 1 point. To do so they have to complete up to 10 bonus point tasks in modules 1-2 and up to 10 bonus point tasks in modules 3-4.

English DSBA 2022/2023

2022-09-12T21:55:28Z

Depressingsupremacy: Поправлены ссылки.

=== [https://docs.google.com/document/d/1TSP_WN02ko4_C19TV9Umpox1TX-_S8ky/edit?usp=sharing Syllabus] ===

=== Teachers and Teaching Assistants ===

{| class="wikitable" style="text-align:center"
| Teachers || Anna Ovchinnikova aovchinnikova@gmail.com || Maria Сoen msokol@bk.ru || Marina Sildimirova aprvip1979@mail.ru
|-
| Groups || [https://docs.google.com/spreadsheets/d/1vziMDDUXNDvg-dY7yArnlJulRwpB9bQb25nvyufJ_fI/edit?usp=sharing 221-1] [https://docs.google.com/spreadsheets/d/1Hx5Nka-mqL8UZ-xXXRctrL27X7GYZRSd3N3MWNY8MGw/edit?usp=sharing 223-1] [https://docs.google.com/spreadsheets/d/1Og2BOIqipNjzuMejUuqye4VkOWv-0bN0qRBsEftRtsI/edit?usp=sharing 224-1] || [https://docs.google.com/spreadsheets/d/1P7CxMmYy0xe6Jo0zadefUncS1UsXDzY7RkZvKUaHShY/edit?usp=sharing 221-2] [https://docs.google.com/spreadsheets/d/1xVR9ppUaMrrFFBpD9m9kvEzEwypOwP9BrD4Vb4C2GJg/edit?usp=sharing 222-2] || [https://docs.google.com/spreadsheets/d/18ZJTYJ6dyLJZL-qih8GIQ6-Qs4cJl-iNukwBko77fyc/edit?usp=sharing 222-1] [https://docs.google.com/spreadsheets/d/10A5ZZ1veckBCyRtfywi9D7ZpNSZp_TVFsfYNJ6VMjPo/edit?usp=sharing 223-2] [https://docs.google.com/spreadsheets/d/1Iq0Kh8QQBb2mSPppSx-Dcn6HOQ6skSRhgL3-Bn6EMwU/edit?usp=sharing 224-2]
|}

{| class="wikitable" style="text-align:center"
| Assistants || Isa Gadaev imgadaev@edu.hse.ru || Daria Lapko dalapko@edu.hse.ru || Natalia Berlina npberlina@edu.hse.ru || Iskander Sergazin Isergazin@edu.hse.ru
|-
| Groups || 223-1 224-1 || 221-2 222-2 || 223-2 224-2 || 221-1 222-1
|}

=== Grading system ===

===== 1-2 modules =====
G 1-2 modules = G cumulative 1-2 modules * 0.6 + G exam 2 module* 0.4 
G cumulative 1-2 modules = G attendance, classwork * 0,4 + G homework * 0,3+ G tests * 0,3

===== 3-4 modules =====

G 3-4 modules = G cumulative 3-4 modules * 0.6 + G mock exam * 0.4 
G cumulative 3-4 modules = G attendance, classwork * 0,4 + G homework * 0,3 + G tests * 0,3

===== Final Grade =====

G final = 0.7 * the lowest mark + 0.3 * the highest mark

''where the lowest and the highest marks are taken from G 1-2 modules and G 3-4 modules.''

The arithmetic rounding rule is applied, i.e. 0.1-0.4 is rounded down and 0.5 and higher is rounded up.

Students can improve their G tests by 1 point. To do so they have to complete up to 10 bonus point tasks in modules 1-2 and up to 10 bonus point tasks in modules 3-4.

English DSBA 2022/2023

2022-09-12T21:54:19Z

Depressingsupremacy: Поправлены ссылки.

=== [https://docs.google.com/document/d/1TSP_WN02ko4_C19TV9Umpox1TX-_S8ky/edit?usp=sharing Syllabus] ===

=== Teachers and Teaching Assistants ===

{| class="wikitable" style="text-align:center"
| Teachers || Anna Ovchinnikova aovchinnikova@gmail.com || Maria Сoen msokol@bk.ru || Marina Sildimirova aprvip1979@mail.ru
|-
| Groups || [https://docs.google.com/spreadsheets/d/1vziMDDUXNDvg-dY7yArnlJulRwpB9bQb25nvyufJ_fI/edit?usp=sharing 221-1] [https://docs.google.com/spreadsheets/d/1Hx5Nka-mqL8UZ-xXXRctrL27X7GYZRSd3N3MWNY8MGw/edit?usp=sharing 223-1] [https://docs.google.com/spreadsheets/d/1Og2BOIqipNjzuMejUuqye4VkOWv-0bN0qRBsEftRtsI/edit?usp=sharing 224-1] || [https://docs.google.com/spreadsheets/d/1P7CxMmYy0xe6Jo0zadefUncS1UsXDzY7RkZvKUaHShY/edit?usp=sharing 221-2] [https://docs.google.com/spreadsheets/d/1xVR9ppUaMrrFFBpD9m9kvEzEwypOwP9BrD4Vb4C2GJg/edit?usp=sharing 222-2] || [https://docs.google.com/spreadsheets/d/1P7CxMmYy0xe6Jo0zadefUncS1UsXDzY7RkZvKUaHShY/edit?usp=sharing 222-1] [https://docs.google.com/spreadsheets/d/10A5ZZ1veckBCyRtfywi9D7ZpNSZp_TVFsfYNJ6VMjPo/edit?usp=sharing 223-2] [https://docs.google.com/spreadsheets/d/1Iq0Kh8QQBb2mSPppSx-Dcn6HOQ6skSRhgL3-Bn6EMwU/edit?usp=sharing 224-2]
|}

{| class="wikitable" style="text-align:center"
| Assistants || Isa Gadaev imgadaev@edu.hse.ru || Daria Lapko dalapko@edu.hse.ru || Natalia Berlina npberlina@edu.hse.ru || Iskander Sergazin Isergazin@edu.hse.ru
|-
| Groups || 223-1 224-1 || 221-2 222-2 || 223-2 224-2 || 221-1 222-1
|}

=== Grading system ===

===== 1-2 modules =====
G 1-2 modules = G cumulative 1-2 modules * 0.6 + G exam 2 module* 0.4 
G cumulative 1-2 modules = G attendance, classwork * 0,4 + G homework * 0,3+ G tests * 0,3

===== 3-4 modules =====

G 3-4 modules = G cumulative 3-4 modules * 0.6 + G mock exam * 0.4 
G cumulative 3-4 modules = G attendance, classwork * 0,4 + G homework * 0,3 + G tests * 0,3

===== Final Grade =====

G final = 0.7 * the lowest mark + 0.3 * the highest mark

''where the lowest and the highest marks are taken from G 1-2 modules and G 3-4 modules.''

The arithmetic rounding rule is applied, i.e. 0.1-0.4 is rounded down and 0.5 and higher is rounded up.

Students can improve their G tests by 1 point. To do so they have to complete up to 10 bonus point tasks in modules 1-2 and up to 10 bonus point tasks in modules 3-4.

English DSBA 2022/2023

2022-09-12T21:52:24Z

Depressingsupremacy: Поправлены ссылки.

=== [https://docs.google.com/document/d/1TSP_WN02ko4_C19TV9Umpox1TX-_S8ky/edit?usp=sharing Syllabus] ===

=== Teachers and Teaching Assistants ===

{| class="wikitable" style="text-align:center"
| Teachers || Anna Ovchinnikova aovchinnikova@gmail.com || Maria Сoen msokol@bk.ru || Marina Sildimirova aprvip1979@mail.ru
|-
| Groups || [https://docs.google.com/spreadsheets/d/1vziMDDUXNDvg-dY7yArnlJulRwpB9bQb25nvyufJ_fI/edit?usp=sharing 221-1] [https://docs.google.com/spreadsheets/d/1vziMDDUXNDvg-dY7yArnlJulRwpB9bQb25nvyufJ_fI/edit?usp=sharing 223-1] [https://docs.google.com/spreadsheets/d/1Og2BOIqipNjzuMejUuqye4VkOWv-0bN0qRBsEftRtsI/edit?usp=sharing 224-1] || [https://docs.google.com/spreadsheets/d/1P7CxMmYy0xe6Jo0zadefUncS1UsXDzY7RkZvKUaHShY/edit?usp=sharing 221-2] [https://docs.google.com/spreadsheets/d/1xVR9ppUaMrrFFBpD9m9kvEzEwypOwP9BrD4Vb4C2GJg/edit?usp=sharing 222-2] || [https://docs.google.com/spreadsheets/d/1xVR9ppUaMrrFFBpD9m9kvEzEwypOwP9BrD4Vb4C2GJg/edit?usp=sharing 222-1] [https://docs.google.com/spreadsheets/d/10A5ZZ1veckBCyRtfywi9D7ZpNSZp_TVFsfYNJ6VMjPo/edit?usp=sharing 223-2] [https://docs.google.com/spreadsheets/d/10A5ZZ1veckBCyRtfywi9D7ZpNSZp_TVFsfYNJ6VMjPo/edit?usp=sharing 224-2]
|}

{| class="wikitable" style="text-align:center"
| Assistants || Isa Gadaev imgadaev@edu.hse.ru || Daria Lapko dalapko@edu.hse.ru || Natalia Berlina npberlina@edu.hse.ru || Iskander Sergazin Isergazin@edu.hse.ru
|-
| Groups || 223-1 224-1 || 221-2 222-2 || 223-2 224-2 || 221-1 222-1
|}

=== Grading system ===

===== 1-2 modules =====
G 1-2 modules = G cumulative 1-2 modules * 0.6 + G exam 2 module* 0.4 
G cumulative 1-2 modules = G attendance, classwork * 0,4 + G homework * 0,3+ G tests * 0,3

===== 3-4 modules =====

G 3-4 modules = G cumulative 3-4 modules * 0.6 + G mock exam * 0.4 
G cumulative 3-4 modules = G attendance, classwork * 0,4 + G homework * 0,3 + G tests * 0,3

===== Final Grade =====

G final = 0.7 * the lowest mark + 0.3 * the highest mark

''where the lowest and the highest marks are taken from G 1-2 modules and G 3-4 modules.''

The arithmetic rounding rule is applied, i.e. 0.1-0.4 is rounded down and 0.5 and higher is rounded up.

Students can improve their G tests by 1 point. To do so they have to complete up to 10 bonus point tasks in modules 1-2 and up to 10 bonus point tasks in modules 3-4.

English DSBA 2022/2023

2022-09-12T21:47:58Z

Depressingsupremacy: Создана страница курса английского ОП ПАД.

=== [https://docs.google.com/document/d/1TSP_WN02ko4_C19TV9Umpox1TX-_S8ky/edit?usp=sharing Syllabus] ===

=== Teachers and Teaching Assistants ===

{| class="wikitable" style="text-align:center"
| Teachers || Anna Ovchinnikova aovchinnikova@gmail.com || Maria Сoen msokol@bk.ru || Marina Sildimirova aprvip1979@mail.ru
|-
| Groups || [https://docs.google.com/spreadsheets/d/1uX-F13gtFrlZBZtUs5UaedKpQw_twb44HpR6QgcmSdI/edit?usp=sharing 221-1] [https://docs.google.com/spreadsheets/d/1Sb2mCty0109omy6e6amksvnVXzDrDQO3oCMnQFdUX9g/edit?usp=sharing 223-1] [https://docs.google.com/spreadsheets/d/1vrntuCQFhHQ_iGYe2HaEeRK-bhJqgdrG4aIiWVfUgE4/edit?usp=sharing 224-1] || [https://docs.google.com/spreadsheets/d/15XpOBw269eFjwtj4j7iyZ0ry4mrtHmfpDhTuTJ0iC30/edit?usp=sharing 221-2] [https://docs.google.com/spreadsheets/d/1itGpLQh1LZ5jS2p1FZPTfZf2rVAw2yS7jIr4zMYxHTg/edit?usp=sharing 222-2] || [https://docs.google.com/spreadsheets/d/1SXDI2Wkyrmrd5yY9__Ogh56mzxSNc5yurJQ906tPh1o/edit?usp=sharing 222-1] [https://docs.google.com/spreadsheets/d/1xNEvRyDlIEVjdmY8V0XkeYCDRGEcqj0tWDqKYPbQ8GM/edit?usp=sharing 223-2] [https://docs.google.com/spreadsheets/d/1xNEvRyDlIEVjdmY8V0XkeYCDRGEcqj0tWDqKYPbQ8GM/edit?usp=sharing 224-2]
|}

{| class="wikitable" style="text-align:center"
| Assistants || Isa Gadaev imgadaev@edu.hse.ru || Daria Lapko dalapko@edu.hse.ru || Natalia Berlina npberlina@edu.hse.ru || Iskander Sergazin Isergazin@edu.hse.ru
|-
| Groups || 223-1 224-1 || 221-2 222-2 || 223-2 224-2 || 221-1 222-1
|}

=== Grading system ===

===== 1-2 modules =====
G 1-2 modules = G cumulative 1-2 modules * 0.6 + G exam 2 module* 0.4 
G cumulative 1-2 modules = G attendance, classwork * 0,4 + G homework * 0,3+ G tests * 0,3

===== 3-4 modules =====

G 3-4 modules = G cumulative 3-4 modules * 0.6 + G mock exam * 0.4 
G cumulative 3-4 modules = G attendance, classwork * 0,4 + G homework * 0,3 + G tests * 0,3

===== Final Grade =====

G final = 0.7 * the lowest mark + 0.3 * the highest mark

''where the lowest and the highest marks are taken from G 1-2 modules and G 3-4 modules.''

The arithmetic rounding rule is applied, i.e. 0.1-0.4 is rounded down and 0.5 and higher is rounded up.

Students can improve their G tests by 1 point. To do so they have to complete up to 10 bonus point tasks in modules 1-2 and up to 10 bonus point tasks in modules 3-4.

Wiki ФКН

2022-09-12T11:17:52Z

Depressingsupremacy: /* Курсы за 2022/23 учебный год */

__NOTOC__

= Учебные курсы факультета компьютерных наук =

== Курсы за 2022/23 учебный год ==

{| class="wikitable"
|-

! 1 курс !! 2 курс !! 3 курс !! 4 курс !! майноры и факультативы
|-
|
'''ПМИ'''

[[Линейная_алгебра_и_геометрия_на_ПМИ_2022/2023_(пилотный_поток) | Линейная алгебра и геометрия (пилотный_поток)]]

[[Линейная_алгебра_и_геометрия_на_ПМИ_2022/2023_(основной_поток) | Линейная алгебра и геометрия (основной поток)]]

[[Математический_анализ_1_2022/2023_(основной_поток) | Математический анализ-1 (основной поток)]]

[[Математический_анализ_1_2022/2023_(пилотный_поток) | Математический анализ-1 (пилотный поток)]]

[[DM1base-2022-23 | Дискретная математика (ПМИ основной поток + ЭАД)]]

[[Дискретная_математика_(пилотный_поток) | Дискретная математика (пилотный поток)]]

[http://wiki.cs.hse.ru/Язык_программирования_Python_2022/2023_(основной_поток) Язык программирования Python 2022/2023 (основной_поток)]

'''ПИ'''

[[Дискретная математика 2022-2023 | Дискретная математика ]]

'''ПАД'''

[[English_DSBA_2022/2023 | English DSBA (DSBA)]]

[[LAaG DSBA 2022/2023 | LAaG (DSBA)]]

[[Discrete Mathematics DSBA 2022/2023 | Discrete Mathematics ]]

[[Introduction to Programming DSBA 2022/2023 | Introduction to Progarmming (DSBA)]]

'''КНАД'''

[[Дискретная_математика_КНАД 22/23| Дискретная математика (КНАД) ]]

[[Математический_анализ_КНАД 22/23| Математический анализ (КНАД) ]]

[[Линейная_алгебра_КНАД 22/23| Линейная алгебра (КНАД) ]]

[[Программирование_на_Python_КНАД 22/23| Программирование на Python (КНАД) ]]
||

'''ПМИ'''

[[Теория_вероятностей_2022/2023_(пилотный_поток)| Теория вероятностей (пилотный поток)]]

[[Теория_вероятностей_2022/2023_(основной_поток)| Теория вероятностей (основной поток)]]

[[Математический_Анализ_2_на_ПМИ_2022/2023_(пилотный_поток) | Математический анализ - 2 (пилотный поток)]]

[[Математический_анализ_-_2_(2022/23) | Математический анализ - 2 (основной поток)]]

[[Язык_программирования_C%2B%2B_(углубленный_курс)_2022 | Язык программирования C++ (углубленный курс)]]

[[Математическая_логика_ПМИ_22/23 | Математическая логика ]]

[[Продвинутый_Python_2022/2023 | Продвинутый Python ]]

[[Алгоритмы_и_структуры_данных_2_2022/2023 | Алгоритмы и структуры данных - 2 ]]

[[Язык_программирования_Rust | Язык программирования Rust ]]

'''ПИ'''

'''ПАД'''

[[Discrete Mathematics 2 DSBA 2022/2023 | Discrete Mathematics 2 (DSBA)]]

[[Business_and_Management_in_Global_Context_22/23 | Business and Management in Global Context 22/23]]

'''КНАД'''

[[Алгебра КНАД 2022/2023 | Алгебра]]

[[Математический_анализ -- 2 (2022/23) | Математический анализ -- 2]]

[[Алгоритмы_и_структуры_данных_2_КНАД 22/23 | Алгоритмы и структуры данных 2]]

[[Теория_вероятностей_КНАД 22/23 | Теория вероятностей ]]
||

'''ПМИ'''

[[Машинное_обучение_1 | Машинное обучение 1]]

[[ Operation Research and Game Theory | Operation Research and Game Theory]]

[[ Statistical learning theory 2022 | Statistical Learning Theory]]

[[ Функциональное_программирование_22-23 | Функциональное программирование ]]

[[ Основы_тензорных_вычислений_(2022/2023) | Основы тензорных вычислений ]]

'''ПМИ / МОП'''

[[Прикладная_статистика_в_машинном_обучении_22/23 | Прикладная статистика в машинном обучении 22/23]]

[[НИС_Машинное_обучение_и_приложения_3_курс_2022/2023 | НИС Машинное обучение и приложения 3 курс]]

'''ПМИ / РС'''

[https://osukhoroslov-hse.notion.site/a35a6a40088b44a7a9e374727a0f2548 Распределенные системы]

[https://osukhoroslov-hse.notion.site/dbce4f5101ed47dca0b363122f139aac НИС Распределенные системы]

'''ПМИ / ТИ'''

[[Theory of Computation 2022 | Theory of Computation]]

[[ NIS-TCS-22-23 | НИС Теоретическая информатика ]]

'''ПИ'''

'''ПАД'''

[[tssp-2022-23 | Time series and stochastic processes 2022-23]]

[https://github.com/xenakas/dsba_ecm_2022 Elements of Econometrics]

||

'''ПМИ'''

[[ Функциональное_программирование_22-23 | Функциональное программирование ]]

[[ Основы_тензорных_вычислений_(2022/2023) | Основы тензорных вычислений ]]

'''ПМИ / МОП'''

[[НИС_Машинное_обучение_и_приложения_4_курс_2022/2023 | НИС Машинное обучение и приложения 4 курс]]

[[Глубинное_обучение_2022 | Глубинное обучение]]

[[Глубинное_обучение_в_анализе_графовых_данных_22/23 | Глубинное обучение в анализе графовых данных]]

'''ПМИ / ТИ'''

[[OWF2022 | Односторонние функции и их применения]]

[[InfTheory2022 | Теория информации]]

[[ NIS-TCS-22-23 | НИС Теоретическая информатика ]]

'''ПИ'''

'''ПАД'''

[[ Statistics4MR-2022-23 | Statistics for Market Research 2022-23]]

||

[[Майнор_Биоинформатика_1_год_2022/23 | Биоинформатика 1 год 2022/23]]

[[Майнор Биоинформатика 2 год 2022/23 | Биоинформатика 2 год]]

[[Основы_глубинного_обучения | Основы глубинного обучения (майнор ИАД)]]

|}

== Курсы в рамках проекта [https://www.hse.ru/dataculture/ Data Culture]==

=== 1 семестр ===
{| class="wikitable"
|-
! Дисциплина !! Образовательная программа !! Курс !! Модули

|-
| [[Цифровая грамотность для международных отношений 22/23 | Цифровая грамотность для международных отношений 22/23]] || || ||

|-
| [http://wiki.cs.hse.ru/%D0%9E%D1%81%D0%BD%D0%BE%D0%B2%D1%8B_%D0%B0%D0%BD%D0%B0%D0%BB%D0%B8%D0%B7%D0%B0_%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85_%D0%B2_%D0%BC%D0%B5%D0%B6%D0%B4%D1%83%D0%BD%D0%B0%D1%80%D0%BE%D0%B4%D0%BD%D1%8B%D1%85_%D0%BE%D1%82%D0%BD%D0%BE%D1%88%D0%B5%D0%BD%D0%B8%D1%8F%D1%85_22/23#.D0.9D.D0.B5.D0.BE.D0.B1.D1.85.D0.BE.D0.B4.D0.B8.D0.BC.D1.8B.D0.B5_.D1.81.D1.81.D1.8B.D0.BB.D0.BA.D0.B8 Основы анализа данных для международных отношений 22/23] || || ||

|-
| [http://wiki.cs.hse.ru/%D0%9E%D1%81%D0%BD%D0%BE%D0%B2%D1%8B_%D0%BF%D1%80%D0%BE%D0%B3%D1%80%D0%B0%D0%BC%D0%BC%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D1%8F_%D0%BD%D0%B0_Python_%D0%BE%D1%81%D0%B5%D0%BD%D1%8C_2022_%D0%BC%D0%B0%D1%82%D1%84%D0%B0%D0%BA#.D0.9F.D1.80.D0.B0.D0.B2.D0.B8.D0.BB.D0.B0_.D0.B4.D0.B5.D0.B4.D0.BB.D0.B0.D0.B9.D0.BD.D0.BE.D0.B2 Основы программирования на Python осень 2022 матфак] || || ||

|-
| [[Анализ данных 2022 (ОП "Журналистика" и "Медиакоммуникации") | Анализ данных]] || Журналистика, Медиакоммуникации || 2 курс || 1-2 модуль

|-
| [http://wiki.cs.hse.ru/%D0%9C%D0%B0%D1%88%D0%B8%D0%BD%D0%BD%D0%BE%D0%B5_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5_(%D0%A4%D0%AD%D0%9D)_-_2022-2023 Машинное обучение] || Экономика || 3,4 курс || 1-2 модули
|}

== Курсы магистратуры ФКН ==
{| class="wikitable"
|-
| [[Ликбез_разработчика_(2022) | Ликбез разработчика]] || Машинное обучение и высоконагруженные системы|| 1 year
|-
| [[RecSys_2022_2023 | Рекомендательные системы]] || ФТИАД || 2 year
|-
| [[Linear_Algebra_for_Data_Science_(2022) | Linear Algebra for Data Science]] || Data Science || 1 year
|-
| [[Ordered_Sets_in_Data_Analysis_(2022) | Ordered Sets in Data Analysis]] || Data Science || 1 year
|-
| [[ NIS-TCS-22-23 | НИС Теоретическая информатика ]] || Науки о данных, специализация ТИ ||
|}

== Курсы других факультетов ==

{| class="wikitable"
|-
! Дисциплина !! Курс !! Период
|-
| [[econ_probability_2022-23|Теория вероятностей и математическая статистика]] || фэн, 2 курс || 1-4 модуль
|-
| [[econ_dynamic_opt_2022-23|Динамическая оптимизация в экономике и финансах]] || фэн, 3-4 курс || 1-2 модуль
|-
| [[icef-dse-2022-23|Data Science for Economics 2022-23]] ||icef, 3-4 year || 1-2 module
|-
| [[Лицей ВШЭ. Практикум по программированию 10 класс 2022-23 | Лицей ВШЭ. Практикум по программированию 10 класс]] || Лицей ВШЭ || 10 класс
|-
|}

= Архив =

== Курсы за 2021/22 учебный год ==
{| class="wikitable"
|-

! 1 курс !! 2 курс !! 3 курс !! 4 курс !! майноры и факультативы
|-
|
'''ПМИ'''

[[Линейная_алгебра_и_геометрия_на_ПМИ_2021/2022_(пилотный_поток) | Линейная алгебра и геометрия (пилотный_поток)]]

[[Линейная_алгебра_и_геометрия_на_ПМИ_2021/2022_(основной_поток) | Линейная алгебра и геометрия (основной поток)]]

[[Алгебра_на_ПМИ_2021/2022_(пилотный_поток) | Алгебра (пилотный поток)]]

[[Алгебра_на_ПМИ_2021/2022_(основной_поток) | Алгебра (основной поток)]]

[[Математический_Анализ_1_на_ПМИ_2021/2022_(пилотный_поток) | Математический анализ 1 (пилотный поток)]]

[[Математический_анализ_1_2021/2022_(основной_поток) | Математический анализ-1 (основной поток)]]

[[DM1-2021-22 | Дискретная математика]]

[http://wiki.cs.hse.ru/Основы_и_методология_программирования_на_ПМИ_2021/2022_(основной_поток) Основы и методология программирования 2021/2022 (основной поток)]

[[Алгоритмы и структуры данных пилотный поток 2021/2022 | Алгоритмы и структуры данных пилотный поток 2021/2022]]

[[Алгоритмы и структуры данных 1 основной поток 2021/2022 (4 модуль) | Алгоритмы и структуры данных 1 основной поток 2021/2022 (4 модуль)]]

'''ПИ'''

[[DM1-SE-2021-22 | Дискретная математика-1 ПИ]]

[http://hsealgebra22.wikidot.com/ Алгебра 2021/2022 ПИ wikidot]

[[Алгебра 2021/2022 ПИ | Алгебра 2021/2022 ПИ]]

'''ПАД'''

[[Algebra_DSBA_2021/2022 | Algebra DSBA]]

[[Discrete Mathematics DSBA 2021/2022 | Discrete Mathematics (DSBA)]]

[[LAaG DSBA 2021/2022 | LAaG (DSBA)]]

[[Calculus DSBA 2021/2022 | Calculus (DSBA)]]

[[Introduction to Programming DSBA 2021/2022 | Introduction to Programming (DSBA)]]

'''КНАД'''

[[Математический_анализ_(КНАД) | Математический анализ (КНАД)]]

[[Линейная алгебра_(КНАД) | Линейная алгебра (КНАД)]]

[[Программирование_на_Python | Программирование на Python (КНАД)]]

[[Дискретная_математика_КНАД | Дискретная математика (КНАД)]]

[[Алгоритмы и структуры данных-1 2021/2022 4 модуль (КНАД) | Алгоритмы и структуры данных (КНАД)]]

[[Программирование_на_С++_КНАД | Программирование на С++ (КНАД)]]

[[Python_для_сбора_и_анализа_данных_КНАД | Python для сбора и анализа данных (КНАД)]]

[[ИПР_КНАД_22 | Инструменты промышленной разработки (КНАД)]]

||

'''ПМИ'''

[[Теория_вероятностей_и_математическая_статистика_2021/2022_(пилотный_поток)| ТВиМС (пилотный поток)]]

[[Теория_вероятностей_2021/2022_(основной_поток)| ТВиМС (основной поток)]]

[[Математический анализ 2021/2022 (пилотный поток) | Математический анализ - 2 (пилотный поток)]]

[http://wiki.cs.hse.ru/%D0%9C%D0%B0%D1%82%D0%B5%D0%BC%D0%B0%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D0%B9_%D0%B0%D0%BD%D0%B0%D0%BB%D0%B8%D0%B7_-_2.1_(2021/22) Математический анализ - 2.1 (основной поток)]

[[Математический_анализ_-_2.2_(2021/22) | Математический анализ - 2.2 (основной поток)]]

[[MissingSemester2021/2022 | Инструменты промышленной разработки (The Missing Semester of your CS education)]]

[[Язык программирования C++ (углубленный курс) | Язык программирования C++ (углубленный курс)]]

[[Математическая логика | Математическая логика]]

[[Дифференциальные уравнения (ПМИ)]]

[[Алгоритмы и структуры данных 2 2021 | Алгоритмы и структуры данных 2]]

[[Алгоритмы_и_структуры_данных_пилотный_поток_2020/2021 | Алгоритмы и структуры данных – 2 на ПМИ (пилотный поток)]]

[[CAOS-2021 | Архитектура компьютеров и операционные системы]]

[[Основы_матричных_вычислений_2021/2022| Основы матричных вычислений 2021/2022]]

'''ПИ'''

[[Алгоритмы и структуры данных на ПИ 2021/2022 | Алгоритмы и структуры данных на ПИ]]

[[Теория вероятностей и математическая статистика 2021-2022 | Теория вероятностей и математическая статистика]]

[[НИС Методы и алгоритмы защиты информации | НИС Методы и алгоритмы защиты информации]]

'''ПАД'''

[[Discrete Mathematics 2 DSBA 2021/2022 | Discrete Mathematics 2 (DSBA)]]

[[Business_and_Management_in_Global_Context_2021/22 | Business and Management in Global Context (DSBA)]]

[[Statistics_DSBA_2021/2022 | Probability and Statistics (DSBA)]]

[[Differential_Equations_2021 | Differential Equations ]]

[[ACOS_DSBA_2021/2022 | Computer Architecture and Operating Systems (DSBA)]]

||

[https://www.notion.so/2021-2022-7374a6eeced2434288226a339bd5037f Курсовая работа]

'''ПМИ'''

[[Intro_to_DL_21/22 | Introduction to Deep Learning 21/22 ]]

[[Безопасность_компьютерных_систем_21/22 | Безопасность компьютерных систем 21/22]]

[http://wiki.cs.hse.ru/%D0%A7%D0%B8%D1%81%D0%BB%D0%B5%D0%BD%D0%BD%D1%8B%D0%B5_%D0%9C%D0%B5%D1%82%D0%BE%D0%B4%D1%8B_2022#.D0.A3.D1.87.D0.B5.D0.B1.D0.BD.D1.8B.D0.B9_.D0.BF.D0.BB.D0.B0.D0.BD Численные методы (2022)]

[[Методы_оптимизации_21/22 | Методы оптимизации 21/22]]

[http://wiki.cs.hse.ru/%D0%9C%D0%B0%D1%82%D0%B5%D0%BC%D0%B0%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%BE%D0%B5_%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5_22 Математическое моделирование]

[http://wiki.cs.hse.ru/NIS-TCS-20-21 НИС Теоретическая информатика]

[[time_series_modelling_21_22 | Моделирование временных рядов 21/22]]

[[Анализ данных в бизнесе (Кафедра SAS) 21/22 | Анализ данных в бизнесе (Кафедра SAS) 21/22]]

[[Comb2021_2022 | Комбинаторные конструкции в теоретической информатике]]

[[Машинное_обучение_1/2021_2022 | Машинное обучение 1]]

[[Theory of Computation 2021 | Theory of Computation]]

[http://math-info.hse.ru/s21/8 Прикладные дифференциальные уравнения]

[[Сбор_и_разметка_данных_для_машинного_обучения_21/22 | Сбор и разметка данных для машинного обучения 21/22]]

[[Промышленное_программирование_на_языке_Java/2022 | Промышленное программирование на языке Java]]

'''ПМИ / PC'''

[https://osukhoroslov-hse.notion.site/5ca71a3b2b17452599f169468df43408 НИС Распределенные системы]

[[Database_Systems_2022 | Database Systems 2022]]

[https://osukhoroslov-hse.notion.site/082aef7073a844afa4c1bf718a773d77 Распределенные системы]

[https://naorlov.notion.site/naorlov/Nets-2021-51931594039e40aeb9102b784d7a8e70 Компьютерные сети]

'''ПМИ / МОП'''

[[Машинное_обучение_2 | Машинное обучение 2]]

[[Методы оптимизации в машинном обучении 2022| Методы оптимизации в машинном обучении 2022]]

[[НИС_Машинное_обучение_и_приложения_3_курс_2021/2022 | НИС Машинное обучение и приложения 3 курс]]

[http://wiki.cs.hse.ru/Psmo_21_22 Прикладная статистика в машинном обучении 21/22]

[[Statistical_learning_theory_2021 | Statistical learning theory]]

'''ПИ'''

[[ML4SE_1 | НИС Машинное обучение для программной инженерии]]

[[Технологии прикладного анализа данных SAS | Технологии прикладного анализа данных SAS]]

'''ПАД'''

[http://wiki.cs.hse.ru/Time_Series_and_Stochastic_Processes_ada_21_22 Time series and stochastic processes 2021-22]

[https://github.com/xenakas/dsba_ecm_2021 Elements of Econometrics ]

[[Data Science Case Studies (JD SAS) 21/22 | Data Science Case Studies (JD SAS) 21/22]]

[[SAS Technologies for Data Mining | SAS Technologies for Data Mining]]
[[Optimization_Methods_2022 | Optimization Methods]]

||

[[Введение_в_дифференциальную_геометрию_2022 | Введение в дифференциальную геометрию 2022]]

[[Символьные_вычисления_21/22 | Символьные вычисления 21/22]]

[[Случайные_процессы_(зима_2022) | Случайные процессы (зима 2022) ]]

[[Методы_сжатия_и_передачи_медиаданных_21/22 | Методы сжатия и передачи медиаданных 21/22]]

[[Теория_и_практика_онлайн-экспериментов_21/22 | Теория и практика онлайн-экспериментов 21/22]]

[[Безопасность_компьютерных_систем_21/22 | Безопасность компьютерных систем 21/22]]

[http://wiki.cs.hse.ru/%D0%94%D0%B8%D0%B7%D0%B0%D0%B9%D0%BD_%D1%81%D0%B8%D1%81%D1%82%D0%B5%D0%BC_21/22 Дизайн систем]

[http://wiki.cs.hse.ru/%D0%9E%D1%81%D0%BD%D0%BE%D0%B2%D1%8B_%D1%82%D0%B5%D0%BD%D0%B7%D0%BE%D1%80%D0%BD%D1%8B%D1%85_%D0%B2%D1%8B%D1%87%D0%B8%D1%81%D0%BB%D0%B5%D0%BD%D0%B8%D0%B9 Основы тензорых вычислений]

[[Сбор_и_разметка_данных_для_машинного_обучения_21/22 | Сбор и разметка данных для машинного обучения 21/22]]

[[Байесовские_методы_машинного_обучения_2021 | Байесовские методы машинного обучения]]

[[НИС_Машинное_обучение_и_приложения_4_курс_2021/2022 | НИС Машинное обучение и приложения 4 курс]]

[https://artemova.notion.site/artemova/c88a68750cff4b509699fa127be11801 Глубинное обучение для текстовых данных 2021/2022]

[http://wiki.cs.hse.ru/LSML_2021/2022 Машинное обучение для больших данных 21/22]

[[Эффективные системы глубинного обучения 21/22|Эффективные системы глубинного обучения]]

'''ПМИ / РС'''

[https://osukhoroslov-hse.notion.site/2-0cd36b67b9f64182bfeabcda9ede647a НИС Распределенные системы 2]

[[msbdp_21 | Методы и системы обработки больших данных]]

[https://osukhoroslov-hse.notion.site/cee4a50ac8cf4e328cefbd35cf822fa5 Облачные вычисления]

'''ПМИ / ТИ'''

[[InfTheory2021 | Теория информации]]

[[OWF2021 | Односторонние функции и их применения]]

[[ConvAppr22 | Выпуклое программирование и аппроксимационные алгоритмы ]]

'''ПАД'''

[[Statistics 4mr 2021-22 | Statistical Methods for Market Research (DSBA)]]

[[Decision Making Strategy]]

||

[[Дополнительные_главы_теории_вероятностей_2022|ДГТВ]]

[[Дополнительные_главы_теории_вероятностей-2_2021/2022|ДГТВ-2]]

[[Теория_вычислений_2022|Теория вычислений]]

[[NonClassicalLogics|Факультатив "Неклассические логики" (3 модуль)]]

[[Криптография_на_решётках_21/22| Криптография на решётках]]

[http://wiki.cs.hse.ru/%D0%9C%D0%B0%D0%B9%D0%BD%D0%BE%D1%80_%D0%91%D0%B8%D0%BE%D0%B8%D0%BD%D1%84%D0%BE%D1%80%D0%BC%D0%B0%D1%82%D0%B8%D0%BA%D0%B0_1_%D0%B3%D0%BE%D0%B4_2021/22 Майнор Биоинформатика 1 год 2021/22]

[[Майнор_Биоинформатика_2_год_2021/22|Майнор Биоинформатика 2 год 2021/22]]

[[Введение_в_программирование_21/22_(майнор_ИАД) | Введение в программирование 21/22 (майнор ИАД)]]

[[Основы_машинного_обучения | Основы машинного обучения (майнор ИАД)]]

[[Основы_глубинного_обучения/2021_2022 | Основы глубинного обучения (майнор ИАД)]]

[[Прикладные_задачи_анализа_данных | Прикладные задачи анализа данных (майнор ИАД)]]

[[Функциональное_программирование_2019 | Функциональное программирование 2021]]

[[KotlinElective|Факультатив "Язык Kotlin" (ноя-дек 2021)]]

[[Dopglavy_DM_2022| Дополнительные главы дискретной математики]]

[[Факультатив_Data_Science_в_игровой_индустрии_(SAS) | Факультатив Data Science в игровой индустрии (SAS)]]

[[Факультатив_Анализ_данных_на_платформе_SAS | Факультатив Анализ данных на платформе SAS]]

[https://rroll.to/2D8yXl Основы и методология пикапа на ПМИ 2021/2022]

|}

== Курсы в рамках проекта [https://www.hse.ru/dataculture/ Data Culture]==

=== 2 семестр ===
{| class="wikitable"
|-
! Дисциплина !! Образовательная программа !! Курс !! Модули
|-
| [http://wiki.cs.hse.ru/%D0%9F%D1%80%D0%BE%D0%B3%D1%80%D0%B0%D0%BC%D0%BC%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5_%D0%BD%D0%B0_%D1%8F%D0%B7%D1%8B%D0%BA%D0%B5_Python,_%D0%B6%D1%83%D1%80%D0%BD%D0%B0%D0%BB%D0%B8%D1%81%D1%82%D0%B8%D0%BA%D0%B0_2022 Программирование на языке Python] || Журналистика || 2 курс || 4 модуль
|-
| [http://wiki.cs.hse.ru/Программирование_на_Python_деп_медиа_2022 Программирование на языке Python] || Журналистика и Медиакоммуникации || 1 курс || 3 модуль
|-
| [http://wiki.cs.hse.ru/Python_для_ОП_%22Психология%22_2021/2022 Программирование на языке Python] ||Психология || 1 курс || 3-4 модуль
|-
| [http://wiki.cs.hse.ru/Python_для_ОП_%22Экономика_и_статистика%22_и_ОП_%22География_глобальных_изменений%22_2021/2022 Программирование на языке Python] ||Экономика и статистика; География глобальных изменений || 2 курс || 3 модуль
|-
| [[Культура работы с данными]] ||Иностранные языки и межкультурная коммуникация || 1 курс || 3-4 модуль
|-
| [[Машинное обучение на матфаке 2022|Машинное обучение]] || Математика || 3-4 модули || 3-4 модули
|-
| [[Введение_в_МО_БИ_21/22| Введение в машинное обучение]] || Бизнес-информатика || 3 курс || 3-4 модули
|-
| [[Excel для анализа данных 21-22]] || Иностранные языки и межкультурная коммуникация || 4 курс || 3 модуль
|-
| [[Введение в Data Science 21-22]] || УБ и МиРА || 2 курс || 4 модуль
|-
| [[Основы программирования в Python (Мирэк) 2022 | Основы программирования в Python]] || Мировая экономика || 2 курс || 3-4 модуль
|-
|[http://wiki.cs.hse.ru/Python_%D0%B4%D0%BB%D1%8F_%D0%B8%D0%B7%D0%B2%D0%BB%D0%B5%D1%87%D0%B5%D0%BD%D0%B8%D1%8F_%D0%B8_%D0%BE%D0%B1%D1%80%D0%B0%D0%B1%D0%BE%D1%82%D0%BA%D0%B8_%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85_%D0%A5%D0%B8%D0%BC%D0%B8%D1%8F Python для извлечения и обработки данных] || Химия || 2 курс || 4 модуль
|-
| [http://wiki.cs.hse.ru/Цифровая_грамотность_Городское_планирование_2021-2022 Цифровая грамотность] || Городское планирование || 1 курс || 4 модуль
|}

=== 1 семестр ===
{| class="wikitable"
|-
! Дисциплина !! Образовательная программа !! Курс !! Модули
|-
| [http://wiki.cs.hse.ru/%D0%A6%D0%B8%D1%84%D1%80%D0%BE%D0%B2%D0%B0%D1%8F_%D0%B3%D1%80%D0%B0%D0%BC%D0%BE%D1%82%D0%BD%D0%BE%D1%81%D1%82%D1%8C_%D0%93%D0%B5%D0%BE%D0%B3%D1%80%D0%B0%D1%84%D0%B8%D1%8F_2020/21 Цифровая грамотность] || География глобальных измерений и геоинформационные технологии || 1 курс || 1-2 модули
|-
| [[Цифровая грамотность, Востоковедение 2021/2022| Цифровая грамотность]] || Востоковедение || 1 курс || 1-2 модули
|-
|[[Цифровая Грамотность ИКВиА 2021/2022 | Цифровая грамотность]] || ИКВиА || 1 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%98%D0%BD%D1%84%D0%BE%D1%80%D0%BC%D0%B0%D1%86%D0%B8%D0%BE%D0%BD%D0%BD%D1%8B%D0%B5_%D1%82%D0%B5%D1%85%D0%BD%D0%BE%D0%BB%D0%BE%D0%B3%D0%B8%D0%B8_%D0%B2_%D0%B4%D0%B5%D1%8F%D1%82%D0%B5%D0%BB%D1%8C%D0%BD%D0%BE%D1%81%D1%82%D0%B8_%D1%8E%D1%80%D0%B8%D1%81%D1%82%D0%B0_2018-2019 Цифровая грамотность] || Юриспруденция || 1 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%98%D0%BD%D1%84%D0%BE%D1%80%D0%BC%D0%B0%D1%86%D0%B8%D0%BE%D0%BD%D0%BD%D1%8B%D0%B5_%D1%82%D0%B5%D1%85%D0%BD%D0%BE%D0%BB%D0%BE%D0%B3%D0%B8%D0%B8_%D0%B2_%D0%B4%D0%B5%D1%8F%D1%82%D0%B5%D0%BB%D1%8C%D0%BD%D0%BE%D1%81%D1%82%D0%B8_%D1%8E%D1%80%D0%B8%D1%81%D1%82%D0%B0_2018-2019 Цифровая грамотность] || Юриспруденция:частное право || 1 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%A6%D0%B8%D1%84%D1%80%D0%BE%D0%B2%D0%B0%D1%8F_%D0%B3%D1%80%D0%B0%D0%BC%D0%BE%D1%82%D0%BD%D0%BE%D1%81%D1%82%D1%8C_%D0%98%D1%81%D1%82%D0%BE%D1%80%D0%B8%D1%8F_2021/2022 Цифровая грамотность] ||История || 1 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/Цифровая_грамотность_Филология_2021/2022 Цифровая грамотность] || Филология || 1 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%A6%D0%B8%D1%84%D1%80%D0%BE%D0%B2%D0%B0%D1%8F_%D0%B3%D1%80%D0%B0%D0%BC%D0%BE%D1%82%D0%BD%D0%BE%D1%81%D1%82%D1%8C_2020-21_(%D0%9A%D1%83%D0%BB%D1%8C%D1%82%D1%83%D1%80%D0%BE%D0%BB%D0%BE%D0%B3%D0%B8%D1%8F) Цифровая грамотность] || Культурология || 1 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%A6%D0%B8%D1%84%D1%80%D0%BE%D0%B2%D0%B0%D1%8F_%D0%B3%D1%80%D0%B0%D0%BC%D0%BE%D1%82%D0%BD%D0%BE%D1%81%D1%82%D1%8C_%D0%94%D0%B5%D0%BF%D0%B0%D1%80%D1%82%D0%B0%D0%BC%D0%B5%D0%BD%D1%82_%D0%9C%D0%B5%D0%B4%D0%B8%D0%B0_2021-2022 Цифровая грамотность] || Медиакоммуникации и Журналистика || 1 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/Цифровая_грамотность_Философия_2021/2022 Цифровая грамотность] || Философия || 1 курс || 1-2 модуль
|-
| [http://wiki.cs.hse.ru/Основы_программирования_на_Python_осень_2021_матфак Основы программирования на Python] || Математика || 3 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%90%D0%BD%D0%B0%D0%BB%D0%B8%D0%B7_%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85_%D0%B2_R,_%D0%A1%D0%BE%D1%86%D0%B8%D0%BE%D0%BB%D0%BE%D0%B3%D0%B8%D1%8F Анализ данных в R] || Социология || 4 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%9F%D0%9C%D0%A1%D0%90%D0%A0-2_2020 Методы анализа больших данных в исследованиях поведения покупателя] || Прикладные методы социального анализа рынков (магистратура) || 2 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%9C%D0%B0%D1%88%D0%B8%D0%BD%D0%BD%D0%BE%D0%B5_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5_(%D0%A4%D0%AD%D0%9D)_-_2021-2022 Машинное обучение] || Экономика || 3,4 курс || 1-2 модули
|-
| [[Программирование_ЭкСтат_ГП | Программирование на языке Python ]] || Городское планирование, Экономика и статистика || 3 курс || 1-2 модули
|-
| [[Основы анализа данных в международных отношениях 2021/2022 | Основы анализа данных в международных отношениях]] || Международные отношения || 2 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/Python_%D0%B4%D0%BB%D1%8F_%D0%B8%D0%B7%D0%B2%D0%BB%D0%B5%D1%87%D0%B5%D0%BD%D0%B8%D1%8F_%D0%B8_%D0%BE%D0%B1%D1%80%D0%B0%D0%B1%D0%BE%D1%82%D0%BA%D0%B8_%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85 Python извлечение и обработка данных] || Клеточная и молекулярная биотехнология || 1 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%9E%D1%81%D0%BD%D0%BE%D0%B2%D1%8B_%D0%BF%D1%80%D0%BE%D0%B3%D1%80%D0%B0%D0%BC%D0%BC%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D1%8F_%D0%B2_Python_(%D0%9F%D0%BE%D0%BB%D0%B8%D1%82%D0%BE%D0%BB%D0%BE%D0%B3%D0%B8%D1%8F_2020) Основы программирования в Python ] || Политология || 3 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/Основы_работы_с_данными:_сбор,_анализ,_визуализация_(ОП_%22Журналистика%22) Основы работы с данными: сбор, анализ, визуализация] || Журналистика || 3 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%92%D0%B2%D0%B5%D0%B4%D0%B5%D0%BD%D0%B8%D0%B5_%D0%B2_Data_Science Data Science] || Экономика впечатлений: менеджмент в индустрии гостеприимства и туризме (магистратура) || 1 курс || 2 модуль
|-
| [http://wiki.cs.hse.ru/%D0%9C%D0%B0%D1%88%D0%B8%D0%BD%D0%BD%D0%BE%D0%B5_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5_(%D1%81%D0%BE%D0%B2._%D0%B1%D0%B0%D0%BA._%D0%92%D0%A8%D0%AD-%D0%A0%D0%AD%D0%A8_2021) Машинное обучение] || Совместный бакалавриат НИУ ВШЭ и РЭШ || 3 курс || 1-2 модули
|-
|
|| Бакалавриат НИУ ВШЭ и Лондонского университета || 3 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/Data_Analysis_in_Economics_and_Finance_2020-2021 Data Analysis in Economics and Finance] || Бакалавриат НИУ ВШЭ и Лондонского университета || 3 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/Data_Analysis_in_Journalism_and_Political_Science_2020-2021 Data Analysis in Journalism and Political Science]|| Бакалавриат НИУ ВШЭ и Лондонского университета || 3 курс || 1-2 модули
|-
| [http://wiki.cs.hse.ru/%D0%9F%D1%80%D0%B8%D0%BA%D0%BB%D0%B0%D0%B4%D0%BD%D1%8B%D0%B5_%D0%B8%D1%81%D1%81%D0%BB%D0%B5%D0%B4%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D1%8F_%D0%B2_%D0%BA%D1%83%D0%BB%D1%8C%D1%82%D1%83%D1%80%D0%BE%D0%BB%D0%BE%D0%B3%D0%B8%D0%B8_2020_1%D0%BC%D0%BE%D0%B4%D1%83%D0%BB%D1%8C Прикладные исследования в культурологии] || Прикладная культурология (магистратура) || 1 курс || 1 модуль
|-
| [http://wiki.cs.hse.ru/%D0%9F%D1%80%D0%BE%D0%B3%D1%80%D0%B0%D0%BC%D0%BC%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5_%D0%B8_%D0%BA%D0%BE%D0%BC%D0%BF%D1%8C%D1%8E%D1%82%D0%B5%D1%80%D0%BD%D1%8B%D0%B5_%D0%B8%D0%BD%D1%81%D1%82%D1%80%D1%83%D0%BC%D0%B5%D0%BD%D1%82%D1%8B_%D0%BB%D0%B8%D0%BD%D0%B3%D0%B2%D0%B8%D1%81%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%BE%D0%B3%D0%BE_%D0%B8%D1%81%D1%81%D0%BB%D0%B5%D0%B4%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D1%8F Программирование и лингвистические данные] || Фундаментальная и компьютерная лингвистика || 1 курс || 1, 2, 3, 4 модули
|-
| [http://wiki.cs.hse.ru/%D0%9F%D1%80%D0%BE%D0%B3%D1%80%D0%B0%D0%BC%D0%BC%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5_%D0%B8_%D0%BA%D0%BE%D0%BC%D0%BF%D1%8C%D1%8E%D1%82%D0%B5%D1%80%D0%BD%D1%8B%D0%B5_%D0%B8%D0%BD%D1%81%D1%82%D1%80%D1%83%D0%BC%D0%B5%D0%BD%D1%82%D1%8B_%D0%BB%D0%B8%D0%BD%D0%B3%D0%B2%D0%B8%D1%81%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%BE%D0%B3%D0%BE_%D0%B8%D1%81%D1%81%D0%BB%D0%B5%D0%B4%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D1%8F Программирование и лингвистические данные] || Фундаментальная и компьютерная лингвистика || 1 курс || 1, 2, 3 модули
|-
| [http://wiki.cs.hse.ru/%D0%9A%D0%BE%D0%BC%D0%BF%D1%8C%D1%8E%D1%82%D0%B5%D1%80%D0%BD%D0%B0%D1%8F_%D0%BB%D0%B8%D0%BD%D0%B3%D0%B2%D0%B8%D1%81%D1%82%D0%B8%D0%BA%D0%B0_%D0%B8_%D0%B8%D0%BD%D1%84%D0%BE%D1%80%D0%BC%D0%B0%D1%86%D0%B8%D0%BE%D0%BD%D0%BD%D1%8B%D0%B5_%D1%82%D0%B5%D1%85%D0%BD%D0%BE%D0%BB%D0%BE%D0%B3%D0%B8%D0%B8 Программирование и теория алгоритмов] || Фундаментальная и компьютерная лингвистика || 3 курс || 2, 3, 4 модули
|-
| [https://github.com/daria-sa/NNmethods_ba_hse21-22/ Нейросетевые методы в обработке текстов] || Фундаментальная и компьютерная лингвистика || 4 курс || 1, 2, 3 модули
|-

|}

== Курсы магистратуры ФКН ==

{| class="wikitable"
|-
| [[Modern_Data_Analysis_2021_2022 | Modern Data Analysis]] || Data Science (Науки о данных) || 2 year
|-
| [[Project_Seminar_2021_2022 | Проектный семинар специализации ТИ]] || Науки о данных, специализация ТИ || 1 year
|-
| [[Theory of Computation 2021 | Theory of Computation]] || Науки о данных, специализация ТИ || 1 year
|-
| [[ConvAppr22 | Выпуклое программирование и аппроксимационные алгоритмы ]] || Науки о данных, специализация ТИ || 1 year
|-
| [[RecSys_2021_2022 | Рекомендательные системы]] || ФТИАД || 2 year
|-
| [[Stochastic_analysis_2021_2022 | Стохастический анализ 2021-2022]] || Math of Machine Learning || 1 year
|-
| [[Reinforcement_learning_2021_2022 | Математические основы обучения с подкреплением 2021-2022]] || Math of Machine Learning || 2 year
|-
| [[Theoretical_Computer_Science_2022 | Theoretical computer science 2021-2022]] || Theoretical computer science || PhD
|-
| [[Машинное_обучение_(современные_методы)-МОиВС-2021-2022 | Машинное обучение (современные методы)]] || Машинное обучение и высоконагруженные системы|| 1 year
|-
| [[Алгоритмы_и_структуры_данных-МОиВС-2021-2022 | Алгоритмы и структуры данных]] || Машинное обучение и высоконагруженные системы|| 1 year
|-
| [[Основы_промышленной_разработки-МОиВС-2021-2022 | Основы промышленной разработки]] || Машинное обучение и высоконагруженные системы|| 1 year
|-
| [[Глубинное обучение-МОиВС-2022-2023 | Глубинное обучение]] || Машинное обучение и высоконагруженные системы|| 1 year
|-
|}

== Курсы других факультетов ==

{| class="wikitable"
|-
! Дисциплина !! Курс !! Период
|-
| [http://wiki.cs.hse.ru/Econ_probability_2021-22 Теория вероятностей и математическая статистика] || фэн, 2 курс || 1-4 модуль
|-
| [[Динамическая оптимизация в экономике и финансах, фэн, 2021/22|Динамическая оптимизация в экономике и финансах]] || фэн, 3-4 курс || 1-2 модуль
|-
|}

= Архив до 2020/21 учебного года включительно =
[[Wiki ФКН/Архив]]