Teaching
Neurocomputing
Level: Master.
Responsability: lectures and exercises.
Course website: https://www.tu-chemnitz.de/informatik/KI/edu/neurocomputing
Materials: https://julien-vitay.net/course-neurocomputing/
Syllabus
- Linear algorithms
- Optimization
- Linear regression
- Linear classification
- Learning theory
- Neural networks
- Multi-layer perceptron
- Modern neural networks
- Computer Vision
- Convolutional neural networks
- Object detection
- Semantic segmentation
- Generative modeling
- Autoencoders
- Restricted Boltzmann machines
- Generative adversarial networks
- Recurrent neural networks
- Recurrent neural networks, LSTM
- Natural Language Processing
- Attentional neural networks
- Self-supervised learning
- Transformers
- Contrastive learning
Deep Reinforcement Learning
Level: Master.
Responsability: lectures and exercises.
Course website: https://www.tu-chemnitz.de/informatik/KI/edu/deeplrl
Materials: https://julien-vitay.net/course-deeprl/
Syllabus
- Tabular RL
- Bandits
- Markov Decision Processes
- Dynamic Programming
- Monte Carlo control
- Temporal Difference
- Function approximation
- Deep learning
- Model-free RL
- Deep Q-network
- Beyond DQN
- Policy Gradient
- A2C / A3C
- DDPG
- TRPO / PPO
- SAC
- Model-based RL
- Model-based RL
- Learned models
- AlphaGo
- Successor representations
Introduction to AI
Level: Bachelor.
Responsability: exercises.
Course website: https://www.tu-chemnitz.de/informatik/KI/edu/ki
Syllabus
- Blind search
- Heuristic search
- Game theory
- Constraint propagation
- Optimization
- Neural networks
- Support vector machines
- Probability theory
- Information theory
- Decision trees
- Estimators
- Reinforcement learning