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Module Artificial Intelligence, Master Course Computer Science (ER 7)
Module summary

Artificial Intelligence

INFM210ML

Prof. Dr. Patrick Baier

7 ECTS points / 6 Contact hours

All semesters

none

none

Individual exams
Course Artificial Intelligence

INFM211ML

Lecture

Prof. Dr. Patrick Baier

German

3/2

Written/verbal Exam 60/20 Min. (graded)

This lecture introduces current developments and research in the field of artificial intelligence and deep learning.


To start with, the foundations of neural networks are shortly repeated to be able to understand the following algorithms that are mainly based on deep learning.

Different architectures are introduced, like "Convolutional Neural Networks", "Recurrent Neural Networks" and "LSTMs", and their application in the context of "Computer Vision", "Natural Language Processing" and "Reinforcement Learning" are presented.

 

The outline of the lecture is as follows:

  • Neural networks and deep learning 
  • CNNs 
  • Object detection, image segmentation
  • Transfer learning
  • Sequential models (RNNs, LSTMs, GRUs)
  • Language models, word embeddings, neural machine translation
  • Attention mechanism and transformer models
  • Reinforcement Learning: Basics, Q-learning, DQNs, Alpha Go
  • Autoencoders and GANs


Course Artificial Intelligence Exercise

INFM212ML

Exercise

M.Sc. Anna Weißhaar
Prof. Dr. Patrick Baier

German

4/4

Concept 1 Semester (graded)

This lab implements the theoretical foundations from the lecture into practical tasks.


For this, tasks from the following three domains are tackled:

* Computer Vision

* Natural Language Processing

* Reinforcement Learning


18

Requirements:

  • Basic knowledge in Python
  • Basic knowledge in Machine Learning