Introduction to Machine Learning and Data Mining

2019-05-30T12:49:15Z

Akorabelnikov: TA's homeworks

'''Lecturers:''' Dmitry Ignatov

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

=== Homeworks ===

Homework 1: Spam classification.

Soft deadline (up to 10 points): <s>March 9</s> March 19

Hard deadline (-2 points): <s>March 15</s> March 25

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===

Intro slides.

Practice: demonstration with Orange.

=== Lecture on 06.02.2019===

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

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.

=== Lecture on 06.03.2019 ===

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.

=== 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.

=== 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.

Introduction to Machine Learning and Data Mining

2019-05-30T12:45:50Z

Akorabelnikov: 2 TA's classes

'''Lecturers:''' Dmitry Ignatov

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

=== Homeworks ===

Homework 1: Spam classification.

Soft deadline (up to 10 points): <s>March 9</s> March 19

Hard deadline (-2 points): <s>March 15</s> March 25

=== Lecture on 23.01.2019===

Intro slides.

Practice: demonstration with Orange.

=== Lecture on 06.02.2019===

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

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.

=== Lecture on 06.03.2019 ===

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.

=== 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.

=== 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.

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Introduction to Machine Learning and Data Mining

Introduction to Machine Learning and Data Mining