Prerequisites: Modules in Mathematics I to IV, basic knowledge in Python.
Exam: oral examination (20 minutes), 5 credit points.
Contact: julien dot vitay at informatik dot tu-chemnitz dot de.
Language: English. The exam can of course be done in German.
Exam in SS2018
The grades have been sent to the administration. You can have a look at your work during my speaking hours (Mondays between 13:00 and 14:00) when I am back from holidays, i.e. after September 17th.
The course will present an introduction to the research field of Machine Learning, including Supervised Learning, Deep Learning and Reinforcement Learning. The different algorithms presented during the lectures will be studied in more details during the exercises, through implementations in Python.
The plan of the course is:
- Supervised learning
- Linear machines
- Learning Theory
- Neural Networks
- Support vector machines
- Deep Learning
- Recurrent neural networks
- Reinforcement Learning
- Formal definition of the RL-Problem
- Dynamic Programming
- Monte Carlo Methods
- Temporal Difference Learning (TD)
- Eligibility traces
- Function approximation
- Deep Reinforcement learning
- "Deep Learning", Goodfellow, Bengio & Courville
- "Support Vector Machines and other kernel-based learning methods", Cristianini & Shawe-Taylor
- "Reinforcement Learning", Sutton & Barto (1st and 2nd editions)
Slides for the lecturesChapter 01 - Introduction (pdf)
Chapter 02 - Linear learning machines (pdf)
Chapter 03 - Learning theory (pdf)
Chapter 04 - Neural networks (pdf)
Chapter 05 - Support-vector machines (pdf)
Chapter 06 - Deep learning (pdf)
Chapter 07 - Recurrent neural networks (pdf)
Chapter 08 - Reinforcement Learning (pdf)
Chapter 09 - Deep Reinforcement Learning (pdf)
ExercisesExercise 01 - Introduction to Python and NumPy. (pdf , data , solution )
Exercise 02 - Linear classification. (pdf , data , solution )
Exercise 03 - Cross-validation. (pdf , data , solution )
Exercise 04 - Multi-layer perceptron. (pdf , data , solution )
Exercise 05 - Multi-layer perceptron on the MNIST dataset. (pdf , data , solution )
Exercise 06 - Support-vector machines. (pdf , data )
Exercise 07 - Convolutional neural networks. (pdf , data , solution )
Exercise 08 - Transfer learning. (pdf , data , solution )
Exercise 09 - Reinforcement learning. (pdf , solution )
Exercise 10 - Q-learning and Gridworld. (pdf , data , solution )