Lecture: Foundations of Artificial Intelligence

Course Number 13548-01
Lecturers Malte Helmert
Gabriele Röger
Assistants Jendrik Seipp
Silvan Sievers
Tutors Daniel Killenberger
Time and Location Mon 16:15 - 18:00; Seminarraum 05.002, Spiegelgasse 5
Wed 14:15 - 16:00; Seminarraum 05.002, Spiegelgasse 5
Start 20-02-2017
Exercises Tue 16:15 - 18:00; Seminarraum 00.003, Spiegelgasse 1
Wed 16:15 - 18:00; Computer-Labor U1.001, Spiegelgasse 1
Group assignment (Requires login)
Prerequisites No formal requirements, but solid basic knowledge of foundational concepts in computer science (algorithms, complexity theory) and mathematics (formal proofs and basic concepts like sets, functions and relations) are necessary for following the lecture. Good programming skills are necessary for some of the exercises.
Objectives Students learn the theoretical and practical foundations of classical problems in artificial intelligence and their algorithmic solution. In particular, participants will obtain the necessary knowledge and skills to independently solve typical AI problems by selecting, implementing and evaluating standard algorithms from the AI literature.
Contents The course offers an introduction into the basic concepts, problems, methods and algorithms of artificial intelligence. Topics include: introduction and historical development of AI, rational agents, problem solving and search, constraint satisfaction problems, formal logic, and automated planning.
Literature Stuart Russell and Peter Norvig: Artificial Intelligence - A Modern Approach (3rd edition), Prentice Hall, 2009.
Assessment Lehrveranst.-begleitend

Please note: oral examination; 28/29/30 June 2017, Spiegelgasse 1, office 06.004.

The course includes weekly homework assignments and weekly exercise sessions. To pass the course, students need to successfully work on the homework assignments and pass the final oral examination. At least 50% of the possible marks from homework assignment are needed to qualify for the final exam. The final grade for the course is based exclusively on the final exam.
Credit Points 8
Grades 1-6 0,5
Modules Vertiefungsmodul Computer Science (Bachelor Informatik 07)
Vertiefungsmodul Bioinformatik (Bachelor Informatik 07)
Modul Wahlbereich Informatik (BSF - Informatik (Studienbeginn vor 01.08.2016))
Modul Praxis aktueller Informatikmethoden (MSF - Informatik (Studienbeginn vor 01.08.2016))
Vertiefungsmodul Computational Intelligence (Bachelor Informatik 10)
Vertiefungsmodul Life Science-Informatik (Bachelor Informatik 10)
Modul Methoden für Computational Biology (Bachelor Computational Sciences 11)
Modul Methoden für Computational Chemistry (Bachelor Computational Sciences 11)
Modul Methoden für Computational Mathematics (Bachelor Computational Sciences 11)
Modul Methoden für Computational Physics (Bachelor Computational Sciences 11)
Modul Machine Intelligence (Bachelor Computer Science 16)
Modul Applications and Related Topics (BSF - Computer Science)
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