- 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)
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 | |
Lecturers | |
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
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
- 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 |