Inhalt Hotkeys

Maschinelles Lernen

Machine learning

Vorlesung: Mittwoch, 19.00 - 20.30, 1/305 (Dr. J. Vitay)
Übung: Dienstag, 13.45 - 15.15, 1/B202 (A. Jamalian)
Übung: Donnerstag, 13.45 - 15.15, 1/B202 (A. Jamalian)

Important. Lecture: room change to 305 after 01.11. However, it will be in 201 on 18.10 and 25.10.

General Information

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 oral exam can of course be done in German.

Office hours: Mondays between 13:00 and 14:00 during the lectures period.

Note: there will be no exam in the summer semester.


The course will present an introduction to the research field of Machine Learning, including Supervised Learning, Unsupervised Learning and Reinforcement Learning methods. The different algorithms presented during the lectures will be studied in more details during the exercises, through implementations in Python. Previous knowledge of Python is a plus.

The plan of the course is:

  1. Pattern recognition
    1. Linear machines
    2. Learning Theory
    3. Neural Networks
    4. Support vector machines
    5. Deep Learning
    6. Recurrent neural networks
  2. Reinforcement Learning
    1. Formal definition of the RL-Problem
    2. Dynamic Programming
    3. Monte Carlo Methods
    4. Temporal Difference Learning (TD)
    5. Eligibility traces
    6. Function approximation
    7. Deep Reinforcement learning



  • I am student of the Master ASE 2016 and machine learning does not appear in the course planner. Can I take the exam?
    Yes. The course planner is not official, you will be able to register for the exam on SBService.
  • 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 will start in December. Individual appointments will be given in January after the registration is closed and I receive the complete list of registered students. Do not write emails before you are told to.
  • 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 important for the exam.
  • Do I have to memorize all these equations?
    No, but to understand them, which is basically the same.
  • I failed the exam in the winter semester. Can I retake it in the summer semester, otherwise I will not be able to finish my Master thesis?
    No, exams are in the winter semester only, no exception. Come prepared to the exam if you are under time pressure.

Slides for the lectures

Chapter 01 - Introduction (pdf)
Chapter 02 - Linear learning machines (pdf)
Chapter 03 - Learning theory (pdf)


Exercise 01 - Introduction to Python and NumPy. (pdf , data )


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