Machine Learning 2025/26

Probability and statistics [slides]

[DFO] 6.1, 6.2, 6.3, 6.4, 6.5

Classification 1 [slides]

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

Tutorial 1 [sheets]

Data sets

Classification 2 [slides]

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

Classification 3 [slides]

[LWLS] 3.2, 3.3

Classification 4 [slides]

[LWLS] 3.2, 3.3

Multivariate calculus

[DFO] 5.2, 5.3

Optimization 1

[SB] 12.1

Optimization 2

[SB] 14.1

Tutorial 2

Digit classification

Optimization 3

[DFO] 7.2

Support vector machines

[DFO] 12.2, 12.3

Features and kernels

[LWLS] 3.3, 8.1

Neural networks 1

[LWLS] 6.1

Neural networks 2

[LWLS] 6.2

Generalization 1

[SB] 3.1, 4.1

CW1 released

Tutorial 3

pytorch

Generalization 2

[SB] 6.2

Generalization 3

[SB] 13.2, 13.3

Linear regression 1

[LWLS] 3.1

Linear regression 2

[LWLS] 3.3, 8.1

Principal component analysis

[B] 12.1

K-means

[B] 9.1
[LWLS] 10.2

CW1 due

Tutorial 4

Singular value decomposition

Gaussian mixture models

[B] 9.2
[LWLS] 10.2

Expectation maximization

[B] 9.3, 9.4

Probabilistic graphical models 1

[B] 8.1, 8.2, 8.3

Probabilistic graphical models 2

[B] 8.1, 8.2, 8.3

Self-supervised learning 1

Self-supervised learning 2

CW1 feedback returned

Tutorial 5

Autoencoders

Generalization 4

Closing