621.065 (18W) Uncertain Knowledge: Reasoning and Learning
Overview
- Lecturer
- Course title german Uncertain Knowledge: Reasoning and Learning
- Type Lecture - Course (continuous assessment course )
- Hours per Week 2.0
- ECTS credits 4.0
- Registrations 24 (25 max.)
- Organisational unit
- Language of instruction German
- possible language(s) of the assessment German , English
- Course begins on 01.10.2018
- eLearning Go to Moodle course
Time and place
Course Information
Intended learning outcomes
Students should recognize the issue of uncertainty inherent in many Artificial Intelligence applications, understand basic methods for dealing with this issue and learn to adopt and comprehend concrete algorithms that implement these methods. The focus in the first half of the semester will be on reasoning under uncertainty, whereas the second half will deal with learning.
Teaching methodology including the use of eLearning tools
Lecture mixed with practical home and in-class exercises. Slides will be in English. Teaching language will be German unless there are non-German-speaking participants, otherwise English.
eLearning
Moodle
Course content
Provides an introduction to selected methods for dealing with uncertainty in Artificial Intelligence and Knowledge-Based Systems.
Topics
- Uncertainty in AI Systems
- Bayesian Inference
- Theory and Construction of Bayesian Networks
- Query Answering and Inference by means of Bayesian Networks
- Unsupervised Machine Learning (Data Mining): Association Rule Mining
- Supervised Machine Learning (Data Mining): Classification
Literature
Adnan Darwiche. Modeling and Reasoning with Bayesian Networks. Cambridge University Press. 2009 P.
Tan, M. Steinbach, V. Kumar. Introduction to Data Mining. Pearson. 2006
Stuart Russell and Peter Norvig: Artificial Intelligence: A modern approach. Prentice Hall, 2009
Judea Pearl: Probabilistic Reasoning in Intelligent Systems - Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc. 1988
D. Koller, N. Friedman. Probabilistic Graphical Models: Principles and Techniques. The MIT Press. 2009
D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press. 2012
T. Mitchell. Machine Learning. McGraw Hill. 1997
Examination information
Examination methodology
written examinations (60%) + homework exercises (40%) + collaboration points (bonus)
Examination topic(s)
theoretical and practical topics discussed in the course
Assessment criteria / Standards of assessment for examinations
Will be published in Moodle at the beginning of the semester.
Grading 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)
-
Knowledge Engineering (
2.0h PR / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h PR / 4.0 ECTS)
-
Subject: Angewandte Informatik (LI 2.3)
(Compulsory subject)
-
Stage two
- Bachelor's degree programme Applied Informatics
(SKZ: 511, Version: 17W.1)
-
Subject: Medieninformatik
(Compulsory elective)
-
4.2 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
4.2 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Subject: Medieninformatik
(Compulsory elective)
- Bachelor's degree programme Applied Informatics
(SKZ: 511, Version: 17W.1)
-
Subject: Natural Language Processing
(Compulsory elective)
-
5.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
5.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Subject: Natural Language Processing
(Compulsory elective)
- Bachelor's degree programme Applied Informatics
(SKZ: 511, Version: 17W.1)
-
Subject: Software Development
(Compulsory elective)
-
6.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
6.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Subject: Software Development
(Compulsory elective)
- Bachelor's degree programme Applied Informatics
(SKZ: 511, Version: 17W.1)
-
Subject: Business Informatics
(Compulsory elective)
-
7.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
7.3 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Subject: Business Informatics
(Compulsory elective)
- Bachelor's degree programme Applied Informatics
(SKZ: 511, Version: 12W.1)
-
Subject: Media Informatics
(Compulsory elective)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Subject: Media Informatics
(Compulsory elective)
- Bachelor's degree programme Applied Informatics
(SKZ: 511, Version: 12W.1)
-
Subject: Natural Language Processing
(Compulsory elective)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Subject: Natural Language Processing
(Compulsory elective)
- Bachelor's degree programme Applied Informatics
(SKZ: 511, Version: 12W.1)
-
Subject: Software Development
(Compulsory elective)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Subject: Software Development
(Compulsory elective)
- Bachelor's degree programme Applied Informatics
(SKZ: 511, Version: 12W.1)
-
Subject: Business Informatics
(Compulsory elective)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Subject: Business Informatics
(Compulsory elective)
- Bachelorstudium Informatik
(SKZ: 521, Version: 09W.3)
-
Subject: Knowledge Engineering
(Compulsory elective)
-
Knowledge Engineering (
2.0h PR / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h PR / 4.0 ECTS)
-
Subject: Knowledge Engineering
(Compulsory elective)
- Master's degree programme Applied Informatics
(SKZ: 911, Version: 13W.1)
-
Subject: Vertiefung Informatik
(Compulsory subject)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Subject: Vertiefung Informatik
(Compulsory subject)
- Masterstudium Informatik
(SKZ: 921, Version: 09W.1)
-
Subject: Knowledge Engineering
(Compulsory elective)
-
Knowledge Engineering (
2.0h PR / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h PR / 4.0 ECTS)
-
Subject: Knowledge Engineering
(Compulsory elective)
- Masterstudium Mathematics
(SKZ: 401, Version: 18W.1)
-
Subject: Informatics
(Compulsory elective)
-
8.4 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
8.4 Uncertain Knowledge: Reasoning and Learning (
2.0h VC / 4.0 ECTS)
-
Subject: Informatics
(Compulsory elective)
- Master's degree programme Technical Mathematics
(SKZ: 401, Version: 13W.1)
-
Subject: Informatik
(Compulsory elective)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
2.0h UE / 4.0 ECTS)
-
Subject: Informatik
(Compulsory elective)
- Erweiterungscurriculum Vertiefung der Informatik
(Version: 16W.1)
-
Subject: Wissensverarbeitung
(Compulsory elective)
-
Knowledge Engineering (
0.0h UE / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS)
-
Knowledge Engineering (
0.0h UE / 4.0 ECTS)
-
Subject: Wissensverarbeitung
(Compulsory elective)
- Master's degree programme Game Studies and Engineering
(SKZ: 992, Version: 17W.2)
-
Subject: Gebundenes Wahlfach
(Compulsory elective)
-
Module: Game Engineering
-
Uncertain Knowledge: Reasoning and Learning (
0.0h VC / 4.0 ECTS)
- 621.065 Uncertain Knowledge: Reasoning and Learning (2.0h VC / 4.0 ECTS) Absolvierung im 1., 2., 3. Semester empfohlen
-
Uncertain Knowledge: Reasoning and Learning (
0.0h VC / 4.0 ECTS)
-
Module: Game Engineering
-
Subject: Gebundenes Wahlfach
(Compulsory elective)
Equivalent courses for counting the examination attempts
-
Sommersemester 2019
- 621.065 VC Uncertain Knowledge: Reasoning and Learning (2.0h / 4.0ECTS)
-
Sommersemester 2018
- 621.065 VC Uncertain Knowledge: Reasoning and Learning (2.0h / 4.0ECTS)
-
Wintersemester 2017/18
- 621.065 VC Uncertain Knowledge: Reasoning and Learning (2.0h / 4.0ECTS)
-
Sommersemester 2017
- 621.065 VC Uncertain Knowledge: Reasoning and Learning (2.0h / 4.0ECTS)
-
Wintersemester 2016/17
- 621.065 VC Uncertain Knowledge: Reasoning and Learning (2.0h / 4.0ECTS)
-
Sommersemester 2016
- 621.065 VC Uncertain Knowledge: Reasoning and Learning (2.0h / 4.0ECTS)
- Wintersemester 2015/16
-
Sommersemester 2015
- 621.065 VK Uncertain Knowledge: Reasoning and Learning (2.0h / 4.0ECTS)
-
Wintersemester 2014/15
- 621.065 VK Uncertain Knowledge: Reasoning and Learning (2.0h / 4.0ECTS)