623.131 (12S) Selected Topics in Artificial Intelligence
Overview
- Lecturer
- Course title german Selected Topics in Artificial Intelligence
- Type Lecture - Colloquia (continuous assessment course )
- Hours per Week 2.0
- ECTS credits 3.0
- Registrations 18
- Organisational unit
- Language of instruction English
- Course begins on 10.06.2012
Time and place
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Course Information
Teaching methodology including the use of eLearning tools
Course comprises lectures interleaved with exercises Student's active participation is desirable and acknowledge in exam marks Students are offered to do a project in teams of up to three members, successfull project score bonus points taken into account in final examCourse content
Advanced heuristic search methodsTopics
- Machine learning from noisy data
- Language processing with DCG grammars
- Bayesian Networks
- Qualitative Modeling and Reasoning
Teaching objective
Introduce some particularly useful and interesting methods of Artificial Intelligence Develop understanding of these techniques sufficient for application in practicePrior knowledge expected
Basics of computer requirements Knowledge of some basics of Artificial Intelligence Basic knowledge of Prolog is helpfulLiterature
Bratko, Prolog Programming for Artificial Intelligence, 4th Edition, Pearson Education, March 2011 (3rd edition of this book is also adequate) Additional: S. Russell, P. Norvig, Artificial Intelligence: a Modern Approach, 3rd. edition, Prentice-HAll, 2009. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition. Elsevier, 2005.Teaching methodology including the use of eLearning tools
Course comprises lectures interleaved with exercises Student's active participation is desirable and acknowledge in exam marks Students are offered to do a project in teams of up to three members, successfull project score bonus points taken into account in final examCourse content
Advanced heuristic search methodsTopics
- Machine learning from noisy data
- Language processing with DCG grammars
- Bayesian Networks
- Qualitative Modeling and Reasoning
Teaching objective
Introduce some particularly useful and interesting methods of Artificial Intelligence Develop understanding of these techniques sufficient for application in practicePrior knowledge expected
Basics of computer requirements Knowledge of some basics of Artificial Intelligence Basic knowledge of Prolog is helpfulLiterature
Bratko, Prolog Programming for Artificial Intelligence, 4th Edition, Pearson Education, March 2011 (3rd edition of this book is also adequate) Additional: S. Russell, P. Norvig, Artificial Intelligence: a Modern Approach, 3rd. edition, Prentice-HAll, 2009. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition. Elsevier, 2005.Examination information
Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.
Assessment criteria / Standards of assessment for examinations
schriftlich
Assessment criteria / Standards of assessment for examinations
writtenGrading scheme
Grade / Grade grading schemePosition in the curriculum
- Teacher training programme Computer Sciences and Computer Sciences Management (Secondary School Teacher Accreditation)
(SKZ: 884, Version: 04W.7)
-
Stage two
-
Subject: Angewandte Informatik (LI 2.3)
(Compulsory subject)
-
Ausgewählte Kapitel aus Artificial Intelligence (
2.0h VK / 4.0 ECTS)
- 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 3.0 ECTS)
-
Ausgewählte Kapitel aus Artificial Intelligence (
2.0h VK / 4.0 ECTS)
-
Subject: Angewandte Informatik (LI 2.3)
(Compulsory subject)
-
Stage two
- Masterstudium Informatik
(SKZ: 921, Version: 09W.1)
-
Subject: Data and Knowledge Engineering
(Compulsory subject)
-
Artificial Intelligence (
2.0h VK / 4.0 ECTS)
- 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 4.0 ECTS)
-
Artificial Intelligence (
2.0h VK / 4.0 ECTS)
-
Subject: Data and Knowledge Engineering
(Compulsory subject)
- Masterstudium Informatik
(SKZ: 921, Version: 09W.1)
-
Subject: Intelligent Information Systems in Production, Operation and Management (POM)
(Compulsory subject)
-
Selected Topics in Intelligent Systems (
2.0h VK / 4.0 ECTS)
- 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 4.0 ECTS)
-
Selected Topics in Intelligent Systems (
2.0h VK / 4.0 ECTS)
-
Subject: Intelligent Information Systems in Production, Operation and Management (POM)
(Compulsory subject)
- Master's degree programme Information Management
(SKZ: 922, Version: 05W.2)
-
Subject: Data and Knowledge Engineering
(Compulsory elective)
-
Spezialgebiete des Informationsmanagements (
4.0h VO, VK / 6.0 ECTS)
- 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 3.0 ECTS)
-
Spezialgebiete des Informationsmanagements (
4.0h VO, VK / 6.0 ECTS)
-
Subject: Data and Knowledge Engineering
(Compulsory elective)
- Master's degree programme Information Management
(SKZ: 922, Version: 05W.2)
-
Subject: Intelligent Information Systems in Production, Operation and Management
(Compulsory elective)
-
Spezialgebiete des Informationsmanagements (
4.0h VO, VK / 6.0 ECTS)
- 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 3.0 ECTS)
-
Spezialgebiete des Informationsmanagements (
4.0h VO, VK / 6.0 ECTS)
-
Subject: Intelligent Information Systems in Production, Operation and Management
(Compulsory elective)
Equivalent courses for counting the examination attempts
-
Sommersemester 2024
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2023
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2022
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2021
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2020
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2019
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
-
Sommersemester 2018
- 623.131 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
-
Sommersemester 2017
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
-
Sommersemester 2016
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
-
Sommersemester 2015
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
-
Sommersemester 2014
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
-
Sommersemester 2013
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)
-
Sommersemester 2011
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)