621.061 (21S) Introduction to Artificial Intelligence I

Sommersemester 2021

Registration deadline has expired.

First course session
05.03.2021 10:00 - 12:00 online Off Campus

... no further dates known

Overview

Due to the COVID-19 pandemic, it may be necessary to make changes to courses and examinations at short notice (e.g. cancellation of attendance-based courses and switching to online examinations).

For further information regarding teaching on campus, please visit: https://www.aau.at/en/corona.

Lecturer

O.Univ.-Prof. Dipl.-Ing. Dr. Gerhard Friedrich

Course title german

Einführung in die Artificial Intelligence I

Type

Lecture - Course (continuous assessment course )

Course model

Online course

Hours per Week

2.0

ECTS credits

3.0

Registrations

27 (30 max.)

Organisational unit

Institut für Artificial Intelligence und Cybersecurity

Language of instruction

English

possible language(s) of the assessment

English

Course begins on

05.03.2021

eLearning

Go to Moodle course

Time and place

Please note that the currently displayed dates may be subject to change due to COVID-19 measures.

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Intended learning outcomes

Provides an introduction to general problem solving methods used in artificial intelligence and knowledge-based systems. The course presents a variety of search approaches as well as modern knowledge representation and reasoning systems implementing them.

Teaching methodology including the use of eLearning tools

Classroom instructions mixed with practical exercises. The teaching language is English or German depending on the preferences of the audience. The slides are in English.

Course content

Covered topics include:

Uninformed and informed search methods
Overview of incomplete (local) approaches to solving hard problems
Knowledge representation and reasoning with constraints programming
MiniZinc programming language
Game playing

Prior knowledge expected

Algorithms and data structures

Literature

Stuart Russell and Peter Norvig: Artificial Intelligence: A modern approach. Prentice Hall, 2009
Rina Dechter: Constraint Processing. Morgan Kaufmann Publishers, 2003
Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2011

Intended learning outcomes

Teaching methodology including the use of eLearning tools

Classroom instructions mixed with practical exercises. The teaching language is English or German depending on the preferences of the audience. The slides are in English.

Course content

Covered topics include:

Uninformed and informed search methods
Overview of incomplete (local) approaches to solving hard problems
Knowledge representation and reasoning with Constraints Programming
MiniZinc programming language
Game playing

Prior knowledge expected

Algorithms and data structures

Literature

Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2011
Rina Dechter: Constraint Processing. Morgan Kaufmann Publishers, 2003
Stuart Russell and Peter Norvig: Artificial Intelligence: A modern approach. Prentice Hall, 2009

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.

German
English

Modified examination information (exceptional COVID-19 provisions)

online

Examination methodology

siehe Moodle

Examination topic(s)

siehe Moodle

Assessment criteria / Standards of assessment for examinations

siehe Moodle

Modified examination information (exceptional COVID-19 provisions)

online

Examination methodology

see moodle

Examination topic(s)

see moodle

Assessment criteria / Standards of assessment for examinations

see moodle

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

Bachelor's degree programme Applied Informatics (SKZ: 511, Version: 19W.2)
- Subject: Vertiefung Informatik (Compulsory elective)
  - 7.3 Einführung in die Artificial Intelligence I ( 2.0h VC / 3.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 3.0 ECTS)
      Absolvierung im 4., 5., 6. Semester empfohlen

Bachelor's degree programme Applied Informatics (SKZ: 511, Version: 17W.1)
- Subject: Medieninformatik (Compulsory elective)
  - 4.1 Heuristic Search ( 2.0h VC / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 3.0 ECTS)

Bachelor's degree programme Applied Informatics (SKZ: 511, Version: 17W.1)
- Subject: Natural Language Processing (Compulsory elective)
  - 5.2 Heuristic Search ( 2.0h VC / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 3.0 ECTS)

Bachelor's degree programme Applied Informatics (SKZ: 511, Version: 17W.1)
- Subject: Software Development (Compulsory elective)
  - 6.2 Heuristic Search ( 2.0h VC / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 3.0 ECTS)

Bachelor's degree programme Applied Informatics (SKZ: 511, Version: 17W.1)
- Subject: Business Informatics (Compulsory elective)
  - 7.2 Heuristic Search ( 2.0h VC / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 3.0 ECTS)

Bachelor's degree programme Applied Informatics (SKZ: 511, Version: 12W.1)
- Subject: Media Informatics (Compulsory elective)
  - Knowledge Engineering ( 2.0h VO / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 3.0 ECTS)

Bachelor's degree programme Applied Informatics (SKZ: 511, Version: 12W.1)
- Subject: Natural Language Processing (Compulsory elective)
  - Knowledge Engineering ( 2.0h VO / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 3.0 ECTS)

Bachelor's degree programme Applied Informatics (SKZ: 511, Version: 12W.1)
- Subject: Software Development (Compulsory elective)
  - Knowledge Engineering ( 2.0h VO / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 3.0 ECTS)

Bachelor's degree programme Applied Informatics (SKZ: 511, Version: 12W.1)
- Subject: Business Informatics (Compulsory elective)
  - Knowledge Engineering ( 2.0h VO / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 3.0 ECTS)

Master's degree programme Applied Informatics (SKZ: 911, Version: 13W.1)
- Subject: Vertiefung Informatik (Compulsory subject)
  - Knowledge Engineering ( 2.0h VO / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 3.0 ECTS)

Masterstudium Mathematics (SKZ: 401, Version: 18W.1)
- Subject: Informatics (Compulsory elective)
  - 8.5 Heuristic Search ( 2.0h VC / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence I (2.0h VC / 2.0 ECTS)

Equivalent courses for counting the examination attempts

Sommersemester 2024

621.061 VC Introduction to Artificial Intelligence 1 - Group A (2.0h / 3.0ECTS)
621.063 VC Introduction to Artificial Intelligence 1 - Group B (2.0h / 3.0ECTS)

Wintersemester 2023/24

621.061 VC Introduction to Artificial Intelligence 1 - Group A (2.0h / 3.0ECTS)
621.063 VC Introduction to Artificial Intelligence 1 - Group B (2.0h / 3.0ECTS)
621.065 VC Introduction to Artificial Intelligence 1 - Group C (2.0h / 3.0ECTS)

Sommersemester 2023

621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)

Wintersemester 2022/23

621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)

Sommersemester 2022

621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)

Wintersemester 2021/22

621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)

Wintersemester 2020/21

621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)

Sommersemester 2020

621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)

Wintersemester 2019/20

621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)