Introduction to Machine Learning and Data Mining - История изменений

Akorabelnikov: TA's homeworks

2019-05-30T12:49:15Z

TA's homeworks

← Предыдущая версия		Версия от 12:49, 30 мая 2019
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	Hard deadline (-2 points): <s>March 15</s> <span style="color:#ff0000">March 25</span>		Hard deadline (-2 points): <s>March 15</s> <span style="color:#ff0000">March 25</span>

			Neural nets homework 1: logistic regression from scratch and first neural net on tensorflow's arithmetics

			Neural nets homework 2: convolutional neural net on tensorflow and it's real-life application

	=== Lecture on 23.01.2019===		=== Lecture on 23.01.2019===

2019-05-30T12:45:50Z

2 TA's classes

← Предыдущая версия		Версия от 12:45, 30 мая 2019
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	Frequent itemsets and association rules. Apriori and FP-growth algorithms. Interestingness measures. Compact representations of frequent itemsets: closed itemsets and association rules.		Frequent itemsets and association rules. Apriori and FP-growth algorithms. Interestingness measures. Compact representations of frequent itemsets: closed itemsets and association rules.
	Applications: taxonomies of web-site users and contextual advertisement.		Applications: taxonomies of web-site users and contextual advertisement.

			=== Lecture on 15.05.2019 ===

			Linear regression (simple regression, multivariative regression), RMS solution, gradient descent solution, Logistic regression, Multilayer neural net, chain rule, Cross-Entropy as loss function, introduciton in convolutional neural nets (convolution, pooling).

			Applications: general purpose regression and classification tasks, computer vision.

			=== Lecture on 29.05.2019 ===

			ConvNets regularization (L1+L2 weight decay, soft labels, early stopping), ConvNet debug(monitoring of metrics and tuning Learning Rate, checklist for debug), Image augmentations, Advanced tips&trics (pseudo-labeling, test-time augmentation, pretraining, Ensemble of nets with SGD), Common image-specific problems (Segmentation: Semantics and Instance, Detection, Identification; their metrics: IoU, mAP).

			Applications: computer vision.

2019-04-02T09:35:14Z

← Предыдущая версия		Версия от 09:35, 2 апреля 2019
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	2. Spectral Clustering for graph partition. Min-cut, Laplace matrix, Fiedler vector. Bipartite spectral clustering.		2. Spectral Clustering for graph partition. Min-cut, Laplace matrix, Fiedler vector. Bipartite spectral clustering.

			=== Lecture on 20.03.2019 ===

			Frequent itemsets and association rules. Apriori and FP-growth algorithms. Interestingness measures. Compact representations of frequent itemsets: closed itemsets and association rules.
			Applications: taxonomies of web-site users and contextual advertisement.

2019-03-09T13:11:53Z

← Предыдущая версия		Версия от 13:11, 9 марта 2019
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	Slides: 1. Clustering. K-means, k-medoids, fuzzy c-means. The number of clusters problem and related heuristics. Hierarchical clustering. Density-based clustering: DBscan and Mean-shift.		Slides: 1. Clustering. K-means, k-medoids, fuzzy c-means. The number of clusters problem and related heuristics. Hierarchical clustering. Density-based clustering: DBscan and Mean-shift.

	2. Spectral Clustering for graph partition. Min-cut, Laplace matrix, Fiedler vector. Bipartite spectral clustering.		2. Spectral Clustering for graph partition. Min-cut, Laplace matrix, Fiedler vector. Bipartite spectral clustering.

2019-03-09T12:58:32Z

← Предыдущая версия		Версия от 12:58, 9 марта 2019
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	Homework 1: Spam classification.		Homework 1: Spam classification.

	Soft deadline (up to 10 points): <s>March 9</s>		Soft deadline (up to 10 points): <s>March 9</s> <span style="color:#ff0000">March 19</span>
	Hard deadline (-2 points): <s>March 15</s>
			Hard deadline (-2 points): <s>March 15</s> <span style="color:#ff0000">March 25</span>

	=== Lecture on 23.01.2019===		=== Lecture on 23.01.2019===

2019-03-09T12:56:24Z

← Предыдущая версия		Версия от 12:56, 9 марта 2019
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	Homework 1: Spam classification.		Homework 1: Spam classification.

	Soft deadline (up to 10 points): <s>March 9<\s>		Soft deadline (up to 10 points): <s>March 9</s>
	Hard deadline (-2 points): <s>March 15<\s>		Hard deadline (-2 points): <s>March 15</s>

	=== Lecture on 23.01.2019===		=== Lecture on 23.01.2019===

2019-03-09T12:56:05Z

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 '''TAs:''' Ivan Zaputliaev (Module 3 and 4), Alexander Korabelnikov (Module 4).
 === Lecture on 23.01.2019===
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 Slides: Decision trees. Entropy and information gain. ID3 algorithm. Gini impurity. Tree pruning.

2019-02-22T07:25:08Z

← Предыдущая версия		Версия от 07:25, 22 февраля 2019
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	'''Lecturers:''' Dmitry Ignatov		'''Lecturers:''' Dmitry Ignatov
	'''
	TAs:''' Ivan Zaputliaev (Module 3 and 4), Alexander Korabelnikov (Module 4).		'''TAs:''' Ivan Zaputliaev (Module 3 and 4), Alexander Korabelnikov (Module 4).

	=== Lecture on 23.01.2019===		=== Lecture on 23.01.2019===

2019-02-22T07:23:52Z

← Предыдущая версия		Версия от 07:23, 22 февраля 2019
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	Lecturers: Dmitry Ignatov		'''Lecturers:''' Dmitry Ignatov
			'''
	TAs: Ivan Zaputliaev (Module 3 and 4), Alexander Korabelnikov (Module 4).		TAs:''' Ivan Zaputliaev (Module 3 and 4), Alexander Korabelnikov (Module 4).

	=== Lecture on 23.01.2019===		=== Lecture on 23.01.2019===

2019-02-22T07:23:39Z

← Предыдущая версия		Версия от 07:23, 22 февраля 2019
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			Lecturers: Dmitry Ignatov

			TAs: Ivan Zaputliaev (Module 3 and 4), Alexander Korabelnikov (Module 4).

	=== Lecture on 23.01.2019===		=== Lecture on 23.01.2019===

	Intro slides		Intro slides.

	Practice: demonstration with Orange.		Practice: demonstration with Orange.
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	=== Lecture on 06.02.2019===		=== Lecture on 06.02.2019===

	Introduction to classification techniques		Slides: Introduction to classification techniques (1-rule, kNN, Naive Bayes, Logistic Regression).

	Practice: demonstration with Orange and scikit-learn.		Practice: demonstration with Orange and scikit-learn.

			=== Lecture on 22.02.2019 ===


			Practice with scikit-learn (kNN, Naive Bayes, Logistic Regression, basic quality metrics, cross-validation, error plots)

			Slides: Decision trees. Entropy and information gain. ID3 algorithm. Gini impurity. Tree pruning.