Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Course INF-71-57-K-6

Very Deep Learning - Recent Methods and Technologies (2V+1U, 4.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
- K Lecture with exercise classes (V/U) 4.0 CP 78 h
2 V Lecture 28 h
1 U Exercise class (in small groups) 14 h
(2V+1U) 4.0 CP 42 h 78 h


CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in WiSe
Level [6] Master (General)
Language [EN] English
Area of study [INF-KI] Intelligent Systems
Livecycle-State [NORM] Active


The lecture wants to dive into very deep learning methods, i.e., the latest state-of-the-art. Therefore it is important that you generally know about Machine Learning and Neural Networks. We suggest having a look at the first online lectures of CS231n: Especially the content of Lectures 1-5 should be known by every attendee.

Also we pose already now the first exercise, it would be good if you start as early as possible to play around with the standard ML toolkits:

At least one of them is mandatory, but for the best learning outcome we recommend to install all three of them.

Possible Study achievement

  • Verification of study performance: proof of successful participation in the exercise classes (ungraded)
  • Details of the examination (type, duration, criteria) will be announced at the beginning of the course.


In this lecture the most recent advances of deep learning will be presented.

The intended schedule is:

  • Introduction, Motivation
  • Advanced Convolutional Networks (ConvNet, AlexNet, GoogLeNet)
  • SqueezeNet
  • Extended Recurrent Neural Networks (LSTM, MD-LSTM, Dynamic Cortex Memories)
  • Spiking Neural Networks
  • Reinforcement Learning (Policy and Value Networks)
  • Bleeding-Edge Architectures (depending on the most recent publications in Deep Learning).



Requirements for attendance (informal)


Requirements for attendance (formal)


References to Course [INF-71-57-K-6]

Module Name Context
[INF-71-57-M-6] Very Deep Learning - Recent Methods and Technologies P: Obligatory 2V+1U, 4.0 LP
Course-Pool Name
[INF-KI_V-KPOOL-6] Lectures of the teaching area Intelligent Systems