Lectures
Lecture 01 - Sep 9 - Basics of linear classification
Lecture 02 - Sep 16 - K-nearest neighbours and Naive Bayes
Lecture 03 - Sep 23 - Linear regression and regularisation
Slides part 1, slides part 2, video
Lecture 04 - Sep 30 - Linear classification
Lecture 05 - Oct 7 - Distance-based and kernel methods
Oct 14 - TEST 1
Lecture 06 - Oct 21 - Decision trees
Lecture 07 - Oct 28 - Evaluation and scoring classifiers
Lecture 08 - Nov 4 - Class probability estimation and logistic regression
Lecture 09 - Nov 11 - Neural networks and deep learning
Slides part 1, slides part 2, video
Nov 18 - TEST 2
Lecture 10 - Nov 25 - Ensemble methods
Slides part 1, slides part 2, video
Lecture 11 - Dec 2 - Probabilistic graphical models
Lecture 12 - Dec 9 - Bayesian machine learning
Lecture 13 - Dec 16 - The world of machine learning - Guest lecturers: Markus Lippus (MindTitan), Mikhail Papkov
Slides, Slides (Markus Lippus), video