Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-75-71-M-7

Advanced Topics in Machine Learning (Seminar) (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
INF-75-71-M-7 Advanced Topics in Machine Learning (Seminar) 4.0 CP (120 h)


CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in WiSe/SuSe
Level [7] Master (Advanced)
Language [EN] English
Module Manager
Area of study [INF-KI] Intelligent Systems
Reference course of study [INF-88.79-SG] M.Sc. Computer Science
Livecycle-State [NORM] Active


Type/SWS Course Number Title Choice in
Presence-Time /
SL SL is
required for exa.
PL CP Sem.
2S INF-75-71-K-7
Advanced Topics in Machine Learning (Seminar)
P 28 h 92 h
- PL1 4.0 WiSe/SuSe
  • About [INF-75-71-K-7]: Title: "Advanced Topics in Machine Learning (Seminar)"; Presence-Time: 28 h; Self-Study: 92 h
  • About [INF-75-71-K-7]: The study achievement "[PRAES] presentation" must be obtained.

Evaluation of grades

The module is not graded (only study achievements)..


Topics are flexible and according to student interests. They may include:
  • Overfitting, regularization, and early stopping
  • Stochastic Optimization
  • Generative approaches (e.g., GANs, Universum Learning)
  • Deep Bayesian learning
  • Sophisticated architectures
  • Piecewise-linear deep networks
  • Globally optimal training of polynomial networks

Competencies / intended learning achievements

Upon successful completion of the module, students will be able to
  • familiarize with a specific topic from the field of Machine Learning,
  • independently compile relevant technical literature on the chosen topic,
  • familiarize themselves thoroughly with a technically and scientifically challenging topic
  • comment on a scientific work in a well-founded and critical manner,
  • place the chosen topic in its scientific context and to differentiate it appropriately
  • present the results in a formally correct, structured and focused way in a written paper,
  • follow and critically question a scientific presentation
  • independently write a scientifically sound written paper on the chosen topic,
  • design and conduct a specialized lecture on the chosen topic in a didactically appealing manner,
  • assess one's own scope for action and decision-making and the associated responsibility and, if necessary, to obtain specific information, define priorities, derive tasks, develop solutions and monitor progress
  • present and discuss a scientific question in English.


  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville.  Deep learning . MIT Press, 2016.
  • Schmidhuber, Jürgen. "Deep learning in neural networks: An overview."  Neural networks  61 (2015): 85-117.
  • Selected research articles

Requirements for attendance of the module (informal)


Requirements for attendance of the module (formal)


References to Module / Module Number [INF-75-71-M-7]

Course of Study Section Choice/Obligation
[INF-88.79-SG] M.Sc. Computer Science [Specialisation] Specialization 1 [WP] Compulsory Elective