Schedule and course plan

Period 2 Where and when Activity Reading Examination Tue 4 Nov

08:00-10:00¶ Q31¶ 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