Course slides are available on Moodle.
Date | Topics | Reading |
---|---|---|
|
Overview of the course
|
Chapter 1 of SS17 |
|
Quiz on Linear Algebra (basics)
|
Chapter 2.1 to 2.11 of here
|
|
Tutorial on Python for ML
|
|
|
Classification and regression
|
Slides |
|
Linear regression and classification
|
Chapter 3.1, 3.2 of DDL2019
Chapters 4.2, 4.3.2 of Bis07 Mitchell's chapter |
Numerical Optimization (basics)
|
Chapter 4.3 of Deep Learning book
Chapters 9.1 to 9.3 of BV excluding convergence proofs |
|
Programming homework on training linear classifiers with various loss functions using stochastic gradient descent | ||
|
Probability for ML (basics)
|
sections 3.1 to 3.9 here
or, chapters 1--6 from PRS, or Chapter 1 and 2 of Bis07) |
|
Probabilistic Generative classifiers
|
Chapter 2.5 of DDL2019 |
|
Support vector machines
|
Chapter 7 of Bis07
Wikipedia |
|
Feedforward Neural networks
|
Chapter 6 of Deep Learning book |
|
Neural Network Architectures: CNNs
| |
|
Neural Architectures for Sequences
|
Chapter 10.0 to 10.4 of Deep Learning book |
|
Combining models
|
Chapter 14.2 of Bis07, Chapter 8.7 of HTF book |
|
Unsupervised learning
|
Lecture notes |
|
Reinforcement Learning
|
|
|
LLMs: Foundation Models for Text
|
|
|
Generative models for images
|