Lectures
Lecture 01 - Sep 10 - Basics of linear classification
Lecture 02 - Sep 17 - K-nearest neighbours and Naive Bayes
Lecture 03 - Sep 24 - Linear regression and regularisation
Lecture 04 - Oct 1 - Linear classification
Lecture 05 - Oct 8 - Distance-based and kernel methods
Oct 15 - TEST 1
Lecture 06 - Oct 22 - Decision trees
Lecture 07 - Oct 29 - Evaluation and scoring classifiers
Lecture 08 - Nov 5 - Class probability estimation and logistic regression
Lecture 09 - Nov 12 - Neural networks and deep learning
Nov 19 - TEST 2
Lecture 10 - Nov 26 - Ensemble methods
Lecture 11 - Dec 3 - Probabilistic graphical models
Lecture 12 - Dec 10 - Bayesian machine learning
Lecture 13 - Dec 17 - The world of machine learning
Guest lecturer Dmitry Zhukov (Transferwise Ltd.): Slides, video