Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-02-11-M-2

Artificial Intelligence (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
INF-02-11-M-2 Artificial Intelligence 4.0 CP (120 h)

Basedata

CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in WiSe
Level [2] Bachelor (Fundamentals)
Language [DE] German
Module Manager
Kloft, Marius, Prof. Dr. (PROF | DEPT: INF)
Lecturers
Dengel, Andreas, Prof. Dr. (PROF | DEPT: INF)
Kloft, Marius, Prof. Dr. (PROF | DEPT: INF)
Lukowicz, Paul, Prof. Dr. (PROF | DEPT: INF)
Area of study [INF-PFL] Mandatory Modules
Reference course of study [INF-82.79-SG] B.Sc. Computer Science
Livecycle-State [NORM] Active

Courses

Type/SWS Course Number Title Choice in
Module-Part 		Presence-Time /
Self-Study 		SL SL is
required for exa. 		PL CP Sem.
2V+1U INF-02-11-K-2
Artificial Intelligence
P 42 h 78 h
U-Schein
ja PL1 4.0 WiSe
  • About [INF-02-11-K-2]: Title: "Artificial Intelligence"; Presence-Time: 42 h; Self-Study: 78 h
  • About [INF-02-11-K-2]: The study achievement [U-Schein] proof of successful participation in the exercise classes (ungraded) must be obtained. It is a prerequisite for the examination for PL1.

Examination achievement PL1

  • Form of examination: written exam (Klausur) (60-90 Min.)
  • Examination Frequency: each semester
  • Examination number: 60211 ("Artificial Intelligence")

Evaluation of grades

The grade of the module examination is also the module grade.


Contents

From [INF-02-11-K-2] Artificial Intelligence:
  • Various types of inference (deduction, induction, abduction)
  • Basics of knowledge modelling and knowledge representation
  • Basics of statistical learning
  • Meaning of the search for the AI
  • Basic concepts for combining statistical and symbolic approaches
  • Knowledge of relevant application areas in practice
  • Examples of complex AI (e.g. Alpha Go)

Competencies / intended learning achievements

The students...
  • develop an understanding of what kind of problems can be solved with the methods of AI,
  • acquire basic skills and knowledge of machine learning and knowledge engineering,
  • can apply methods of machine learning and knowledge engineering to specific problems,
  • develop an understanding of the advantages and disadvantages of different search and problem solving strategies,
  • are able to assess the performance of certain techniques for the respective problem domain using meaningful criteria,
  • can assess the risks involved in developing systems with strong AI.

Literature

From [INF-02-11-K-2] Artificial Intelligence:
  • T. Mitchell, Machine Learning , International edition. New York, NY: Mcgraw-Hill Education Ltd, 1997.
  • C. Beierle und G. Kern-Isberner, Methoden wissensbasierter Systeme: Grundlagen, Algorithmen, Anwendungen , 5. Aufl. Wiesbaden: Springer Vieweg, 2014.
  • W. Ertel, Grundkurs Künstliche Intelligenz: Eine praxisorientierte Einführung , 4. Aufl. Wiesbaden: Springer Vieweg, 2016.
  • S. J. Russell und P. Norvig, Artificial Intelligence , Global ed of 3rd Revised ed. Boston Columbus Indianapolis New York San Francisco: Prentice Hall International, 2017.

Requirements for attendance (informal)

None

Requirements for attendance (formal)

None

References to Module / Module Number [INF-02-11-M-2]

Course of Study Section Choice/Obligation
[INF-82.79-SG] B.Sc. Computer Science Computer Science Systems [P] Compulsory
[MAT-82.105-SG] B.Sc. Mathematics Subsidiary Subject (Minor) [WP] Compulsory Elective
[WIW-82.176-SG] B.Sc. Business Administration and Engineering specialising in Computer Science Engineering specialization - Computer Science [WP] Compulsory Elective
Module-Pool Name
[MV-MBINFO-MPOOL-6] Wahlpflichtmodule Maschinenbau mit angewandter Informatik