621.062 (24W) Introduction to Artificial Intelligence 1 - Group B

Wintersemester 2024/25

Registration possible from
29.08.2024 00:00

First course session
08.10.2024 10:00 - 11:30 HS 2 On Campus
Next session:
15.10.2024 10:00 - 11:30 HS 2 On Campus

Overview

Lecturer
Course title german Introduction to Artificial Intelligence 1 - Group B
Type Lecture - Course (continuous assessment course )
Hours per Week 2.0
ECTS credits 3.0
Registrations 0 (30 max.)
Organisational unit
Language of instruction Englisch
Course begins on 08.10.2024

Time and place

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Course Information

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

Curricular registration requirements

Nothing 

Literature

  • Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2011
  • Rina Dechter: Constraint Processing. Morgan Kaufmann Publishers, 2003
  • Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2012

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

Curricular registration requirements

Nothing

Literature

  • Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2011
  • Rina Dechter: Constraint Processing. Morgan Kaufmann Publishers, 2003
  • Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2012

Link to further information

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.

Examination methodology

Mini tests, written and oral examinations

Examination topic(s)

  • 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  

Assessment criteria / Standards of assessment for examinations

  • The final grade is positive if and only if:
    • >= 50% of all mini test points are reached.
    • >= 40 % in the written exam are reached.
    • The oral examination is positive. 
  • Based on the oral examination, the final grading is set.

Examination methodology

Mini tests, written and oral examinations

Examination topic(s)

  • 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  

Assessment criteria / Standards of assessment for examinations

  • The final grade is positive if and only if:
    • >= 50% of all mini test points are reached.
    • >= 40 % in the written exam are reached.
    • The oral examination is positive. 
  • Based on the oral examination, the final grading is set. 

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

  • Master's degree programme Management, Economics, and Data Science (SKZ: 946, Version: 23W.1)
    • Subject: Minitrack 7: Artificial Intelligence and Machine Learning (Compulsory elective)
      • 13.1 AIML1: Introduction to Artificial Intelligence I ( 0.0h VC / 4.0 ECTS)
        • 621.062 Introduction to Artificial Intelligence 1 - Group B (2.0h VC / 4.0 ECTS)
          Absolvierung im 2-4. Semester empfohlen
  • 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)
        • 621.062 Introduction to Artificial Intelligence 1 - Group B (2.0h VC / 3.0 ECTS)
          Absolvierung im 4., 5., 6. Semester empfohlen
  • Bachelor's degree programme Management Information Systems (SKZ: 522, Version: 20W.2)
    • Subject: Spezialisierung Angewandte Informatik (Compulsory elective)
      • Spezialisierung Angewandte Informatik ( 0.0h VO, VC, KS, UE / 6.0 ECTS)
        • 621.062 Introduction to Artificial Intelligence 1 - Group B (2.0h VC / 3.0 ECTS)
          Absolvierung im 6. Semester empfohlen
  • Bachelor's degree programme Robotics and Artificial Intelligence (SKZ: 295, Version: 22W.1)
    • Subject: Artificial Intelligence (Compulsory subject)
      • 4.1 Introduction to Artificial Intelligence I ( 2.0h VC / 3.0 ECTS)
        • 621.062 Introduction to Artificial Intelligence 1 - Group B (2.0h VC / 3.0 ECTS)

Equivalent courses for counting the examination attempts

Wintersemester 2024/25
  • 621.061 VC Introduction to Artificial Intelligence 1 - Group A (2.0h / 3.0ECTS)
  • 621.063 VC Introduction to Artificial Intelligence 1 - Group C (2.0h / 3.0ECTS)
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)
Sommersemester 2021
  • 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)