Introduction to Machine Learning and Data Mining

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Lecturers: Dmitry Ignatov

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


Homework 1: Spam classification.

Soft deadline (up to 10 points): March 9 March 19

Hard deadline (-2 points): March 15 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.