621.062 (24W) Introduction to Artificial Intelligence 1 - Group B
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
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
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 schemePosition 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
-
13.1 AIML1: Introduction to Artificial Intelligence I (
0.0h VC / 4.0 ECTS)
-
Subject: Minitrack 7: Artificial Intelligence and Machine Learning
(Compulsory elective)
- 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
-
7.3 Einführung in die Artificial Intelligence I (
2.0h VC / 3.0 ECTS)
-
Subject: Vertiefung Informatik
(Compulsory elective)
- 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
-
Spezialisierung Angewandte Informatik (
0.0h VO, VC, KS, UE / 6.0 ECTS)
-
Subject: Spezialisierung Angewandte Informatik
(Compulsory elective)
- 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)
-
4.1 Introduction to Artificial Intelligence I (
2.0h VC / 3.0 ECTS)
-
Subject: Artificial Intelligence
(Compulsory subject)
Equivalent courses for counting the examination attempts
- Wintersemester 2024/25
- Sommersemester 2024
- Wintersemester 2023/24
-
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)