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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)

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

13:15-15:00

Q33

Lecture 4: Markov Models and Hidden Markov Models

Jens Lagergren

Murphy Chapter 17 (17.3-17.5, 17.6.6)

Thu 13 Nov

13:15-15:00

E2

Lecture 5: Undirected Graphical Models

Jens Lagergren

Murphy Chapter 19 (19.1-19.3, 19.4.1, 19.4.3)

Tue 18 Nov

15:15-17:00

Q33

Exercise 2: Lectures 4 and 5

Jens Lagergren

Wed 19 Nov

13:15-15:00

Q36

Lecture 6: Kernel Methods

Carl Henrik Ek

Murphy Chapter 14, 12, own material

Tue 25 Nov

08:15-10:00

E2

Lecture 7: Gaussian Process Regression

Carl Henrik Ek

Murphy Chapter 1, 7, 15, own material

Tue 25 Nov

Assignment 1

Thu 27 Nov

13:15-15:00

E2

Exercise 3: Lectures 6 and 7

Carl Henrik Ek

Mon 1 Dec

10:15-12:00

Q34

Lecture 8: Neural Networks Murphy Chapter 16.5, 27, 28, papers

Wed 3 Dec EARLIER

08:15-10:00

Q31

Lecture 9: Learning Representations Murphy Chapter 1, 16, 28, papers

Mon 8 Dec EARLIER

10:15-12:00

V22

Exercise 4: Lectures 8 and 9

Carl Henrik Ek
Thur Dec 4 Oral examinations Dec 4-5 Assignment 2

Wed 10 Dec

15:15-17:00

Q31

Lecture 10: Topic Models

Hedvig Kjellström

Murphy Chapter 10.4, 27, papers

Mon 15 Dec

10:15-12:00

Q31

Lecture 11: Practical Machine Learning

Hedvig Kjellström

papers

Tue 16 Dec

08:15-10:00

Q31

Exercise 5: Lectures 10 and 11

Hedvig Kjellström
Tue 16 Dec Assignment 3

Wed 17 Dec

15:15-17:00

Q31

Lecture 12: Method and Model Selection

Hedvig Kjellström

Murphy Chapter 1 revisited, papers
Thu 18 Dec Project