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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
13:15-15:00
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 Assignment 1 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
08:15-10:00
Q31
Lecture 9: Learning Representations Murphy Chapter 1, 16, 28, papers Mon 8 Dec
10:15-12:00
V22
Exercise 4: Lectures 8 and 9
Carl Henrik Ek Tue 9 Dec 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