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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)

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.

Content

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. 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

Literature

FAQ

  • 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)
Chapter 04 - Neural networks (pdf)
Chapter 05 - Support-vector machines (pdf)
Chapter 06 - Deep learning (pdf)
Chapter 07 - Recurrent neural networks (pdf)

Exercises

Exercise 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 )
Exercise 06 - Support-vector machines. (pdf , data )

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