Data Science & Intelligent Analytics PT
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Intelligent Analytics and Artificial Intelligence

level of course unit

Master course

Learning outcomes of course unit

Students understand the concept of artificial intelligence (AI). They are familiar with the basic underlying concepts and know/understand various implementation approaches for AI. Furthermore, they understand the significance of data and algorithms with respect to implementation and are able to independently implement simple applications.

prerequisites and co-requisites

not specified

course contents

Students learn about the fundamental techniques and concepts within the context of intelligent operating systems, specifically in the following areas:

-Reasoning (goal trees, rule-based expert systems)
-Search (depth-first, hill climbing, beam, optimal, branch and bound, A*, games, minimax and alpha-beta)
-Constraint (search, domain reduction, visual object recognition)
-Learn (neural nets, back propagation, genetic algorithms, sparse spaces, phonology, near misses, felicity conditions, support vector machines, boosting)
-Representation (classes, trajectories, transitions)
-Usage of AI within the context of business

recommended or required reading

- Runkler, T. A. (2016) Data Analytics: Models and Algorithms for Intelligent Data Analysis. 2. Auflage, Springer Vieweg, Wiesbaden (ISBN: 978-3658140748).
- Russell, S.; Norvig, P. (2016) Artificial Intelligence: A Modern Approach, Global Edition. 3. Auflage, Addison Wesley, Boston (ISBN: 978-1292153964).
- Winson, P. H. (1992) Artificial Intelligence. 3. Auflage, Pearson (ISBN: 978-0201533774).

assessment methods and criteria

Seminar thesis or final examination

language of instruction

English

number of ECTS credits allocated

3

course-hours-per-week (chw)

2

planned learning activities and teaching methods

Lecture with discussion
Interactive workshop
Case studies

semester/trimester when the course unit is delivered

4

name of lecturer(s)

Head of studies

year of study

2

recommended optional program components

N.A.

course unit code

DPR.9

type of course unit

ILV

mode of delivery

In-class course

work placement(s)

N.A.

Kontaktpersonen

Mitarbeiterfoto Karsten Böhm
Prof. (FH) Dipl.-Informatiker Karsten Böhm
Director of Studies
+43 5372 71819 133
Karsten.Boehmfh-kufstein.ac.at
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Förderer

Mit Unterstützung von Bund, Land und Europäischer Union: