Machine learning 1 DSBA 2023/2024 - История изменений

Buntar29 в 16:46, 1 октября 2023

2023-10-01T16:46:43Z

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	== Course description ==		== 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. The introductory experience in machine learning for FSL track is required.		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. The introductory experience in machine learning for FSL track is required.

	== General information ==		== General information ==

Buntar29: /* Course description */

2023-10-01T16:46:19Z

Course description

← Предыдущая версия		Версия от 16:46, 1 октября 2023
Строка 1:		Строка 1:
	== Course description ==		== 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.		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. The introductory experience in machine learning for FSL track is required.

	== General information ==		== General information ==

Buntar29: /* General information */

2023-10-01T16:44:34Z

General information

← Предыдущая версия		Версия от 16:44, 1 октября 2023
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	* Petr Belonovskii		* Petr Belonovskii
	* Elizaveta Kurlovich		* Elizaveta Kurlovich
			* Artem Astashkin

	== Grading formula ==		== Grading formula ==

Buntar29: /* General information */

2023-10-01T16:41:47Z

General information

← Предыдущая версия		Версия от 16:41, 1 октября 2023
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	\| '''ISL''' \|\| [https://www.hse.ru/en/org/persons/223985242 Alexey Boldyrev]		\| '''ISL''' \|\| [https://www.hse.ru/en/org/persons/223985242 Alexey Boldyrev]
	\|-		\|-
	\| '''FSL''' \|\| [https://www.hse.ru/en/org/persons/190892428 ~~Maxim~~ Karpov]		\| '''FSL''' \|\| [https://www.hse.ru/en/org/persons/190892428 Maksim Karpov]
	\|-		\|-
	\|}		\|}
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	! Group !! ISL group 1 !! ISL group 2 !! ISL group 3 !! FSL		! Group !! ISL group 1 !! ISL group 2 !! ISL group 3 !! FSL
	\|-		\|-
	\| '''Professor''' \|\| [https://www.hse.ru/org/persons/401667013 Tatiana Yazykova] \|\| [https://www.hse.ru/staff/kvbykov Kirill Bykov] \|\| [https://www.hse.ru/en/org/persons/190892428 ~~Maxim~~ Karpov] \|\| [https://www.hse.ru/staff/ramazyant Tigran Ramazyan]		\| '''Professor''' \|\| [https://www.hse.ru/org/persons/401667013 Tatiana Yazykova] \|\| [https://www.hse.ru/staff/kvbykov Kirill Bykov] \|\| [https://www.hse.ru/en/org/persons/190892428 Maksim Karpov] \|\| [https://www.hse.ru/staff/ramazyant Tigran Ramazyan]
	\|-		\|-
	\| Seminar date/time (room) \|\| Sat 14:40 (D207) \|\| Thu 09:30(D207) \|\| TBA \|\| Tue 13:00(R608)		\| Seminar date/time (room) \|\| Sat 14:40 (D207) \|\| Thu 09:30(D207) \|\| TBA \|\| Tue 13:00(R608)

Buntar29: /* General information */

2023-10-01T16:41:09Z

General information

← Предыдущая версия		Версия от 16:41, 1 октября 2023
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	{\| class="wikitable"		{\| class="wikitable"
	\|-		\|-
	! Group !! ISL group 1 !! ISL group 2 !! FSL		! Group !! ISL group 1 !! ISL group 2 !! ISL group 3 !! 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]		\| '''Professor''' \|\| [https://www.hse.ru/org/persons/401667013 Tatiana Yazykova] \|\| [https://www.hse.ru/staff/kvbykov Kirill Bykov] \|\| [https://www.hse.ru/en/org/persons/190892428 Maxim Karpov] \|\| [https://www.hse.ru/staff/ramazyant Tigran Ramazyan]
	\|-		\|-
	\| Seminar date/time (room) \|\| Sat 14:40 (D207) \|\| Thu 09:30(D207) \|\| Tue 13:00(R608)		\| Seminar date/time (room) \|\| Sat 14:40 (D207) \|\| Thu 09:30(D207) \|\| TBA \|\| Tue 13:00(R608)
	\|}		\|}

Depressingsupremacy: Первая версия страницы курса

2023-10-01T16:34:38Z

Первая версия страницы курса

Новая страница

== 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''' <br>
0.3 * Home assignments + 0.2 * Quizzes + 0.2 * Test + 0.1 * Participation + 0.2 * Exam
<br><br>
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)