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 16 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
 Chapter 6 and 12 of DDL book 

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
