Teaching

Deep Reinforcement Learning

Level:

Master

Responsability:

Lectures and exercises

Semester:

Winter

Course website:

https://www.tu-chemnitz.de/informatik/KI/edu/deeprl

Materials:

https://julien-vitay.net/course-deeprl/

1. Markov Decision Processes
2. Dynamic Programming, Monte Carlo control, Temporal Difference
3. Deep Q-network
4. Policy Gradient
5. A2C / A3C
6. DDPG
7. TRPO / PPO
8. SAC
9. Model-based RL
10. Learned World models
11. AlphaGo

Introduction to AI

Level:

Bachelor

Responsability:

Exercises

Semester:

Summer

Course website:

https://www.tu-chemnitz.de/informatik/KI/edu/ki

1. Blind search
2. Heuristic search
3. Game theory
4. Constraint propagation
5. Optimization
6. Neural networks
7. Support vector machines
8. Probability theory
9. Information theory
10. Decision trees
11. Estimators
12. Reinforcement learning

Proseminar AI

Level:

Bachelor

Responsability:

Module

Semester:

Summer

Course website:

https://www.tu-chemnitz.de/informatik/KI/edu/prosem

Neurocomputing (formerly)

Level:

Master

Responsability:

None since 2023

Semester:

Winter

Course website:

https://www.tu-chemnitz.de/informatik/KI/edu/neurocomputing

Materials:

https://julien-vitay.net/course-neurocomputing/

1. Optimization
2. Linear regression / classification
3. Multi-layer perceptron
4. Convolutional neural networks
5. Object detection, semantic segmentation
6. Autoencoders
7. Generative adversarial networks
8. RNN, LSTM
9. Attentional neural networks
10. Transformers
11. Contrastive learning