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Schedule and course plan

Period 2

Where and when	Activity	Reading	Examination
Tue 4 Nov 08:15-10:00 Q31	Lecture 1: Introduction Hedvig Kjellström	Murphy Chapter 1
Wed 5 Nov 13:15-15:00 V32	Lecture 2: Graphical Models Jens Lagergren	Murphy Chapter 10 (except 10.2.4, 10.2.5, 10.4), 26.3.1, 26.3.2
Thu 6 Nov 13:15-15:00 Q36	Lecture 3: Mixture Models and the EM Algorithm Jens Lagergren	Murphy Chapter 11 (11.1, 11.2.1-11.2.3, 11.4-11.4.2.5, 11.4.2.8, 11.4.4, 11.4.7), 2.8.1, 2.8.2
Tue 11 Nov 13:15-15:00 E3	Exercise 1: Lectures 2 and 3 Jens Lagergren
Wed 12 Nov 13:00-15:00 Q33	Lecture 4: Markov Models and Hidden Markov Models Jens Lagergren	Murphy Chapter 17 (17.3-17.5, 17.6.6)
Thu 13 Nov 13:00-15:00 E2	Lecture 5: Undirected Graphical Models Jens Lagergren	Murphy Chapter 19 (19.1-19.3, 19.4.1, 19.4.3)
Tue 18 Nov 15:00-17:00 Q33	Exercise 2: Lectures 4 and 5 Jens Lagergren
			Assignment 1
Wed 19 Nov 13:00-15:00 Q36	Lecture 6: Kernel Methods Carl Henrik Ek	Murphy Chapter 14, 12, own material
Tue 25 Nov 08:00-10:00 E2	Lecture 7: Gaussian Process Regression Carl Henrik Ek	Murphy Chapter 1, 7, 15, own material
Thu 27 Nov 13:00-15:00 E2	Exercise 3: Lectures 6 and 7 Carl Henrik Ek
Mon 1 Dec 10:00-12:00 Q34	Lecture 8: Neural Networks	Murphy Chapter 16.5, 27, 28, papers
Wed 3 Dec 08:00-10:00 Q31	Lecture 9: Learning Representations	Murphy Chapter 1, 16, 28, papers
Mon 8 Dec 10:00-12:00 V22	Exercise 4: Lectures 8 and 9 Carl Henrik Ek
			Assignment 2
Wed 10 Dec 15:00-17:00 Q31	Lecture 10: Topic Models Hedvig Kjellström	Murphy Chapter 10.4, 27, papers
Mon 15 Dec 10:00-12:00 Q31	Lecture 11: Practical Machine Learning Hedvig Kjellström	papers
Tue 16 Dec 08:00-10:00 Q31	Exercise 5: Lectures 10 and 11 Hedvig Kjellström
			Assignment 3
Wed 17 Dec 15:00-17:00 Q31	Lecture 12: Method and Model Selection Hedvig Kjellström	Murphy Chapter 1 revisited, papers
			Project