Inhalt Hotkeys

Maschinelles Lernen

Machine learning

Vorlesung: Montag, 17:15 - 18:45, 1/305 (Dr. J. Vitay)
Übung: Mittwoch, 09:15 - 10:45, 1/B202 (R. Larisch)
Übung: Mittwoch, 15:30 - 17:00, 1/B202 (Dr. M. Teichmann)
Übung: Freitag, 11:30 - 13:00, 1/B202 (Dr. M. Teichmann)


The exercise on Friday 21.12 (just before the Christmas holidays) will be cancelled.

General Information

Prerequisites: Modules in Mathematics I to IV, basic knowledge in Python.

Exam: written examination (90 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.


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:

  1. Supervised learning
    1. Linear algorithms (regression, classification, softmax, maximum likelihood)
    2. Learning Theory (cross-validation, VC dimension, feature space)
    3. Neural Networks (MLP, regularization)
    4. Support vector machines (maximum margin classifier, kernel trick)
    5. Deep Learning (CNN, GAN)
    6. Recurrent neural networks (LSTM, GRU)
  2. Reinforcement Learning
    1. Formal definition of the RL-Problem (Markov Decision Processes)
    2. Dynamic Programming, Monte Carlo Methods
    3. Temporal Difference Learning (TD, Q-learning), Eligibility traces
    4. Deep Reinforcement learning (DQN, A3C, DDPG)



  • How do I register for the course?
    You don't. There is no OPAL or anything, just show up to the lectures/exercises and register for the exam in December.
  • How do I register for the exam?
    Registration on SBService happens in December. Only registered students can participate to the exam.
  • I cannot assist to the exercises. Can I take the exam anyway?
    Yes. The exercises are there to help you understand the concepts seen in the lectures and get practical experience with machine learning tools. But they are not obligatory for the exam.
  • Do I have to memorize all these equations?
    No, but to understand them, which is basically the same.

Slides for the lectures

Chapter 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)


To use Jupyter notebooks in the B202, follow these guidelines (pdf).

Exercise 01 - Introduction to Python and NumPy. (questions , solution )
Exercise 02 - Linear classification. (questions , solution )
Exercise 03 - Cross-validation. (questions , solution )
Exercise 04 - Multi-layer perceptron. (questions )
Exercise 05 - Multi-layer perceptron on the MNIST dataset. (questions )