Grundlagen Intelligenter Systeme

Grundlagen Intelligenter Systeme (AI-III)


  • Dr. Torsten Zesch

Practice Classes

  • Nicolai Erbs


The course assumes familiarity with basic computing concepts, but will not assume any knowledge of the Python language or linguistics, which will be acquired during the course. These skills are helpful and will enhance our discussions. If you like to work with your own notebook, we kindly ask you to follow the installation instructions given at


Please use TUCaN to register for the lecture and the exam.


  • Lecture: Monday 9:50 – 11:20 in S2/02 C120
  • Practice class: Monday 11:40 – 13:10 in S2/02 D017
  • Tutorial: TBA


  • TBA

Course content

The lecture offers an introduction into the perspectives, problems, methods and techniques of text technology. All examples and tutorials are based on the programming language Python.

Key aspects:

  • Natural language processing (NLP)
    • Tokenizing
    • Segmentation
    • Part-of-Speech Tagging
    • Corpora
    • Statistical analysis
  • Machine Learning
    • Categorization and classification
    • Information Extraction
  • Introduction to Python
    • Data Structures
    • Library NLTK
    • Structured Programming

The course is based on the Python programming language together with an open source library called the Natural Language Toolkit (NLTK). NLTK allows explorative and problem-solving learning of theoretical concepts without the requirement of extensive programming knowledge.


  • Steven Bird, Ewan Klein, Natural Language Processing with Python, ISBN:978-0596516499

Office Hour

  • TBA

What You Can Expect from Us

  • interactive lecture with integrated tutorials
  • problem based and explorative learning
  • stimulating environment
  • web page, moodle

What we expect from you

  • commitment
  • feedback
  • active participation

Module-Guide Computer Science

If you like to have a jump start on NLTK, have a look at this video.