Lectures
Lecture 01 - Feb 13 - Introduction to machine learning
Video of the lecture (without the last 4 minutes, video stuck at minutes 15-22)
Video of the lecture (without the slides but including last 4 minutes)
Lecture 02 - Feb 20 - Tasks, models, features
Video of the lecture (without the slides - laptop was not recording due to technical problems)
Lecture 03 - Feb 27 - Binary classification and related tasks
Lecture 04 - Mar 6 - Linear regression and regularisation
Lecture 05 - Mar 13 - Linear classification
Video of the lecture (part II)
Mar 20 - TEST 1
Lecture 06 - Mar 27 - Distance-based and kernel methods
Lecture 07 - Apr 3 - Decision trees
Lecture 08 - Apr 10 - Class probability estimation and logistic regression
Lecture 09 - Apr 17 - Neural networks and deep learning
Apr 24 - TEST 2
May 1 - NATIONAL HOLIDAY
Lecture 10 - May 8 - Ensemble methods
Lecture 11 - May 15 - Probabilistic graphical models
Lecture 12 - May 22 - Gaussian processes (guest lecturer: Dmytro Fishman)
Lecture 13 - May 29 - Final lecture
Part 1: Machine learning in the wild (guest lecturer: Markus Lippus from MindTitan)
Part 2: The world of machine learning