Modern Data Analysis 2021 2022 - История изменений

Machine: /* Homeworks */

2021-11-01T11:34:54Z

Homeworks

← Предыдущая версия		Версия от 11:34, 1 ноября 2021
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	'''Announced''': 11.10.2021		'''Announced''': 11.10.2021

	'''Soft deadline''': 03.11.2021		'''Soft deadline''': changed to 08.11.2021 due to pandemic regulations (before it was 03.11.2021)

	'''Hard deadline''': 10.11.2021		'''Hard deadline''': 10.11.2021


	=== Lectures ===		=== Lectures ===

Machine в 10:06, 20 октября 2021

2021-10-20T10:06:29Z

← Предыдущая версия		Версия от 10:06, 20 октября 2021
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	=== Lectures ===		=== Lectures ===

	== Lecture 1==		==== Lecture 1====

	Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch?		Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch?
Строка 29:		Строка 29:
	Practice: demonstration with Orange.		Practice: demonstration with Orange.

	== Lecture 2==		==== Lecture 2====

	Classification. One-rule. Naïve Bayes. kNN. Logistic Regression. Train-test split and cross-validation. Quality Metrics (TP, FP, TN, FN, Precision, Recall, F-measure, Accuracy).		Classification. One-rule. Naïve Bayes. kNN. Logistic Regression. Train-test split and cross-validation. Quality Metrics (TP, FP, TN, FN, Precision, Recall, F-measure, Accuracy).
Строка 35:		Строка 35:
	Practice: demonstration with Orange.		Practice: demonstration with Orange.

	== Lecture 3==		==== Lecture 3====

	Classification (continued). Quality metrics. ROC curves.		Classification (continued). Quality metrics. ROC curves.
Строка 41:		Строка 41:
	Practice: demonstration with Orange.		Practice: demonstration with Orange.

	== Seminar 1==		==== Seminar 1====

	Classification		Classification
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	== Lecture 4==		==== Lecture 4====

	Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan		Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan

	Practice: DBScan Demo.		Practice: DBScan Demo.

Machine в 10:05, 20 октября 2021

2021-10-20T10:05:05Z

← Предыдущая версия		Версия от 10:05, 20 октября 2021
Строка 14:		Строка 14:
	* Homework 1: Classification		* Homework 1: Classification

	Announced: 11.10.2021		'''Announced''': 11.10.2021

	Soft deadline: 03.11.2021		'''Soft deadline''': 03.11.2021

	Hard deadline: 10.11.2021		'''Hard deadline''': 10.11.2021

	=== Lecture 1===
			=== Lectures ===

			== Lecture 1==

	Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch?		Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch?
Строка 26:		Строка 29:
	Practice: demonstration with Orange.		Practice: demonstration with Orange.

	=== Lecture 2===		== Lecture 2==

	Classification. One-rule. Naïve Bayes. kNN. Logistic Regression. Train-test split and cross-validation. Quality Metrics (TP, FP, TN, FN, Precision, Recall, F-measure, Accuracy).		Classification. One-rule. Naïve Bayes. kNN. Logistic Regression. Train-test split and cross-validation. Quality Metrics (TP, FP, TN, FN, Precision, Recall, F-measure, Accuracy).
Строка 32:		Строка 35:
	Practice: demonstration with Orange.		Practice: demonstration with Orange.

	=== Lecture 3===		== Lecture 3==

	Classification (continued). Quality metrics. ROC curves.		Classification (continued). Quality metrics. ROC curves.
Строка 38:		Строка 41:
	Practice: demonstration with Orange.		Practice: demonstration with Orange.

	=== Seminar 1===		== Seminar 1==

	Classification		Classification
Строка 45:		Строка 48:


	=== Lecture 4===		== Lecture 4==

	Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan		Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan

	Practice: DBScan Demo.		Practice: DBScan Demo.

Machine: /* Homeworks */

2021-10-20T10:02:55Z

Homeworks

← Предыдущая версия		Версия от 10:02, 20 октября 2021
Строка 14:		Строка 14:
	* Homework 1: Classification		* Homework 1: Classification

			Announced: 11.10.2021

			Soft deadline: 03.11.2021

			Hard deadline: 10.11.2021

	=== Lecture 1===		=== Lecture 1===

Machine: Новая страница: «== Course: Modern Data Analysis (2021–2022) == '''Lecturer:''' Dmitry Ignatov '''TA:''' TBA All the material are available via our telegram channel. '''Fin…»

2021-10-18T06:07:23Z

Новая страница: «== Course: Modern Data Analysis (2021–2022) == '''Lecturer:''' Dmitry Ignatov '''TA:''' TBA All the material are available via our telegram channel. '''Fin…»

Новая страница

== Course: Modern Data Analysis (2021–2022) ==

'''Lecturer:''' Dmitry Ignatov

'''TA:''' TBA

All the material are available via our telegram channel.

'''Final mark formula''': FM = 0.8 Homeworks + 0.2 Exam (under voting)

=== Homeworks ===

* Homework 1: Classification

=== Lecture 1===

Intro slides. Course plan. Assessment criteria. ML&DM libraries. What to read and watch?

Practice: demonstration with Orange.

=== Lecture 2===

Classification. One-rule. Naïve Bayes. kNN. Logistic Regression. Train-test split and cross-validation. Quality Metrics (TP, FP, TN, FN, Precision, Recall, F-measure, Accuracy).

Practice: demonstration with Orange.

=== Lecture 3===

Classification (continued). Quality metrics. ROC curves.

Practice: demonstration with Orange.

=== Seminar 1===

Classification

Practice: scikit-learn.

=== Lecture 4===

Introduction to Clustering. Taxonomy of clustering methods. K-means. K-medoids. Fuzzy C-means. Types of distance metrics. Hierarchical clustering. DBScan

Practice: DBScan Demo.