Machine Learning 2023/24

Tutorial 1

What is machine learning?

Analytic geometry [slides, notes]

[DFO] 3.3, 3.4, 3.8

Classification 1 [slides]

[LWLS] 3.2

Multivariate calculus [slides, notes]

[DFO] 5.2, 5.3

Classification 2 [slides]

[LWLS] 3.3

Linear regression [slides]

[LWLS] 3.1

Tutorial 2

Curve fitting

Optimization 1 [slides, notes]

[SB] 12.1

Optimization 2 [slides]

[SB] 14.1

Representation and kernels [slides]

[LWLS] 3.3 and 8.1

Neural networks 1 [slides]

[LWLS] 6.1

Neural networks 2 [slides]

[LWLS] 6.2

Tutorial 3

pytorch [answers]

Optimization 3 [slides]

[DFO] 7.2

Support vector machines [slides]

[DFO] 12.2, 12.3

Generalization 1 [slides]

[SB] 3.1, 4.1

CW1 released [sheets, answers]

Generalization 2 [slides]

[SB] 6.2

Principal component analysis [slides]

[B] 12.1

K-means clustering [slides]

[B] 9.1
[LWLS] 10.2

Tutorial 4

SVD

Gaussian mixture models [slides]

[B] 9.2
[LWLS] 10.2

Expectation maximization [slides]

[B] 9.3, 9.4

Ethics [slides]

CW1 due at noon

Probabilistic graphical models 1 [slides]

[B] 8.1, 8.2

Probabilistic graphical models 2 [slides]

[B] 8.3

Generalization 3 [slides]

[SB] 13.2, 13.3

Tutorial 5

Data sets

Matrix factorization [slides]

[LWLS] 10.4

CW1 feedback returned