Machine Learning 2024/25

Tutorial 1 [sheets]

What is machine learning?

Crash course on maths [slides, notes]

[DFO] 5.2, 5.3

Regression 1 [slides]

[LWLS] 3.1

Regression 2 [slides]

[LWLS] 3.3 and 8.1

Ethics [slides]

Classification 1 [slides]

[M1] 9.1, 9.2.1, 9.2.2, 9.2.3, 9.2.4

Tutorial 2 [sheets]

Curve fitting

Classification 2

[M1] 9.1, 9.2.1, 9.2.2, 9.2.3, 9.2.4

Classification 3 [slides]

[LWLS] 3.2, 3.3

Classification 4

[LWLS] 3.2, 3.3

Optimization 1 [slides]

Optimization 2 [slides, notes]

[SB] 12.1

Optimization 3 [slides]

[SB] 14.1

Tutorial 3 [sheets]

Data sets

Optimization 4 [slides]

[DFO] 7.2

Support vector machines [slides]

[DFO] 12.2, 12.3

Neural networks 1 [slides]

CW1 released [sheets, solutions]

[LWLS] 6.1

Neural networks 2 [slides]

[LWLS] 6.2

Neural networks 3 [slides]

Principal component analysis [slides]

[B] 12.1

Tutorial 4 [sheets]

pytorch

K-means clustering [slides]

[B] 9.1
[LWLS] 10.2

Gaussian mixture models [slides]

[B] 9.2
[LWLS] 10.2

CW1 due

Expectation maximization [slides]

[B] 9.3, 9.4

Probabilistic graphical models [slides]

[B] 8.1, 8.2, 8.3

Generalization 1 [slides]

[SB] 3.1, 4.1

Tutorial 5 [tutorial-5.tar.gz]

Singular Value Decomposition

Generalization 2 [slides]

[SB] 6.2

Generalization 3 [slides]

[SB] 13.2, 13.3

Generalization 4 [slides]

CW1 feedback returned

High-dimensional statistics [slides]

Closing [slides]