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This entry is from Winter semester 2020/21 and might be obsolete. You can find a current equivalent here.

CS 592 — Artificial Intelligence
(dt. Künstliche Intelligenz)

Level, degree of commitment Specialization module, compulsory elective module
Forms of teaching and learning,
workload		 Lecture (2 SWS), recitation class (2 SWS),
180 hours (60 h attendance, 120 h private study)
Credit points,
formal requirements		 6 CP
Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
Examination type: Oral or written examination
Language,
Grading		 German,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Computer Science.
Duration,
frequency		 One semester,
Jedes zweite Wintersemester
Person in charge of the module's outline Prof. Dr. Alfred Ultsch

Contents

  • Programming in Prolog/ Predicate Logic /Constraints
  • Knowledge, knowledge representation, inference
  • Structure of knowledge-based systems
  • Probability-based closing
  • DS and Fuzzy Inference
  • Knowledge Engineering and Machine Learning
  • Non-classical Logics
  • Practice of knowledge-based systems/agent systems

Qualification Goals

The students shall

  • learn skills and knowledge of the most important AI methods and their application in practice,
  • knowledge-based inference systems in predicate logic (Prolog),
  • be able to use forms of knowledge representation,
  • have knowledge of problem solving, search and planning algorithms,
  • an overview of common methods of estimation: Bayes, Demster/Shafer, Fuzzy Inference,
  • To know methods of knowledge acquisition from the field of machine learning and knowledge engineering,
  • have an insight into non-classical logic,
  • practice scientific working methods (recognizing, formulating, solving problems, training the ability to abstract),
  • practice oral communication skills in the exercises by practicing free speech in front of an audience.

Prerequisites

None. The competences taught in the following modules are recommended: Object-oriented Programming, either Algorithms and Data Structures or Practical Informatics II: Data Structures and Algorithms for Pre-Service-Teachers, System Software and Computer Communication, either Declarative Programming or Concepts of Programming Languages for Pre-Service-Teachers, Knowledge Discovery.


Applicability

Module imported from M.Sc. Computer Science.

It can be attended at FB12 in study program(s)

  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Mathematics
  • M.Sc. Business Mathematics
  • LAaG Computer Science

When studying B.Sc. Computer Science, this module can be attended in the study area Compulsory Elective Modules in Computer Science.

The module is assigned to Practical Computer Science. Further information on eligibility can be found in the description of the study area.


Recommended Reading

  • W.F. Clocksin, C.S. Mellish: Programming in Prolog, Springer, 2003.
  • S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 2002.



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2020/21. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.