Machine Learning 2022/23

Time

Monday: 3:10pm—4:00pm at 40 George Square Lecture Theatre C
Thursday: 3:10pm—4:00pm at 40 George Square Lecture Theatre B
Friday: 3:10pm—4:00pm at 40 George Square Lecture Theatre B

Exam

The exam is scheduled on 1pm—3pm, Tuesday, 13 Dec, 2022 at St Leonards Land Gym 2. Please check the timetables for the latest date and time.

Links

Instructors

TAs

Drop-in sessions

Reading

Below is a list of books for your reference. Relevant chapters and sections will be listed in the schedule. Reading is optional, but highly recommended.

Schedule

Week Session Date Content Lecturer
1 1 19 Sep bank holiday
2 22 Sep

Introduction [slides]

[B] Chap. 1
[M1] Chap. 1
[HTF] Chap. 1 & 2

Hao
3 23 Sep

Probability [slides] [feedback]

Tutorial 1: Vector-Matrix Calculus [sheets] [feedback]

[B] Chap. 2
[DFO] Chap. 6
[M1] Chap. 2—5

Hao
2 4 26 Sep

Linear regression [slides] [feedback]

[B] Chap. 3
[DFO] Chap. 9
[HTF] Chap. 3
[M1] Chap. 11

Hao
5 29 Sep

Linear classification [slides] [feedback]

[B] Chap. 4
[HTF] Chap. 4
[M1] Chap. 10

Hao
6 30 Sep

Information theory [slides] [feedback]

[M1] Chap. 6
[M2] Chap. 5

Hiroshi
3 7 3 Oct

Tasks [slides] [feedback]

 Hao
8 6 Oct

Optimization 1 [slides] [feedback]

[M1] Sec. 8.1
[SB] Chap. 12

Hao
9 7 Oct

Optimization 2 [slides] [feedback]

Tutorial 2: Curve Fitting [sheets] [script] [feedback]

[M1] Sec. 8.2 & 8.4
[SB] Chap. 12

Hao
4 10 10 Oct

Neural networks 1 [slides] [feedback]

[M1] Sec. 13.1 & 13.2

Hiroshi
11 13 Oct

Optimization 3 [slides] [feedback]

[M1] Sec. 8.5

Hao
12 14 Oct

Neural networks 2 [slides] [feedback]

[M1] Sec. 13.3 & 13.4
[M2] Sec. 6.2

Hiroshi
5 13 17 Oct

Neural networks 3 [slides] [feedback]

[M1] Chap. 14 & Chap. 15
[M2] Sec. 16.3

Hiroshi
14 20 Oct

High-dimensional statistics [slides] [feedback]

[HTF] Sec. 18.1
[MRT] Sec. 15.4

Hao
15 21 Oct

Generalization 1 [slides] [feedback]

Tutorial 3: Computation Graphs [sheets] [code] [feedback]

[MRT] Chap. 2
[SB] Chap. 3—5

Hao
6 16 24 Oct

Generalization 2 [slides] [feedback]

CW1 released [sheets, data, answer]

[MRT] Chap. 3
[SB] Chap. 6

Hao
17 27 Oct

Generalization 3 [slides] [feedback]

[MRT] Chap. 14
[SB] Chap. 13

Hao
18 28 Oct

Generalization 4 [slides] [feedback]

[MRT] Sec. 5.4

Hao
7 19 31 Oct

Principal component analysis [slides] [feedback]

[B] Sec. 12.1
[DFO] Chap. 10
[HTF] Sec. 14.5
[M1] Sec. 20.1
[MRT] Sec. 15.1
[SB] Chap. 23

Kia
20 3 Nov

K-means [slides] [feedback]

[B] Sec. 9.1
[HTF] Sec. 14.3.6
[M1] Sec. 21.3
[SB] Chap. 22

Kia
21 4 Nov

Gaussian mixture models [slides] [feedback]

Tutorial 4: PCA and Eigenfaces [sheets] [code] [feedback]

[B] Sec. 9.2
[DFO] Sec. 11.1
[HTF] Sec. 14.3.7
[M1] Sec. 21.4

Kia
8 22 7 Nov

Expectation maximization [slides] [feedback]

CW1 due

[B] Sec. 9.3 & 9.4
[DFO] Sec. 11.3
[HTF] Sec. 14.3.7
[M1] Sec. 8.7
[M2] Sec. 6.6

Kia
23 10 Nov

Support vector machines [slides] [feedback]

Practice exam released [sheets] [answer]

[B] Sec. 7.1
[DFO] Chap. 12
[HTF] Chap. 12
[M1] Sec. 17.3
[MRT] Chap. 5
[SB] Chap. 15

Hiroshi
24 11 Nov

Boosting [slides] [feedback]

[B] Sec. 14.3
[HTF] Chap. 10
[M1] Sec. 18.5
[MRT] Chap. 7
[SB] Chap. 10

Hiroshi
9 25 14 Nov

Statistical dependencies 1 [slides] [feedback]

[B] Sec. 8.1
[M2] Sec. 4.2

Hao
26 17 Nov

Statistical dependencies 2 [slides] [feedback]

[B] Sec. 8.3
[M2] Sec. 4.3

Hao
27 18 Nov

Ethics [slides] [feedback]

Tutorial 5: Feedback on practice exam [sheets] [feedback]

[M1] Chap. 22

Kia
10 28 21 Nov

Closing [slides] [feedback]

CW1 feedback returned

 Hao
29 24 Nov Industrial action
30 25 Nov Industrial action
11 28 Nov
30 Nov
2 Dec
12 5 Dec
7 Dec
9 Dec
13 12 Dec
14 Dec
16 Dec
14 19 Dec
21 Dec
23 Dec