FI:PA026 AI Project - Course Information

PA026 Artificial Intelligence Project

Extent and Intensity

0/2. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: k (colloquium). Other types of completion: z (credit).

Teacher(s)

doc. RNDr. Aleš Horák, Ph.D. (lecturer)

Guaranteed by

prof. Ing. Václav Přenosil, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: doc. RNDr. Aleš Horák, Ph.D.
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics

Timetable

Mon 14:00–15:50 B411

Prerequisites (in Czech)

PB016 Introduction to AI

Course Enrolment Limitations

The course is also offered to the students of the fields other than those the course is directly associated with.

fields of study / plans the course is directly associated with

• Applied Informatics (programme FI, N-AP)
• Information Technology Security (programme FI, N-IN)
• Bioinformatics (programme FI, N-AP)
• Information Systems (programme FI, N-IN)
• Informatics (programme FI, M-IN)
• Informatics (programme FI, N-IN)
• Parallel and Distributed Systems (programme FI, N-IN)
• Computer Graphics (programme FI, N-IN)
• Computer Networks and Communication (programme FI, N-IN)
• Computer Systems (programme FI, N-IN)
• Embedded Systems (eng.) (programme FI, N-IN)
• Embedded Systems (programme FI, N-IN)
• Service Science, Management and Engineering (eng.) (programme FI, N-AP)
• Service Science, Management and Engineering (programme FI, N-AP)
• Social Informatics (programme FI, B-AP)
• Theoretical Informatics (programme FI, N-IN)
• Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
• Upper Secondary School Teacher Training in Informatics (programme FI, M-TV)
• Upper Secondary School Teacher Training in Informatics (programme FI, N-SS) (2)
• Artificial Intelligence and Natural Language Processing (programme FI, N-IN)
• Image Processing (programme FI, N-AP)

Course objectives

The aim of the seminar is to provide students with a deeper knowledge concerning a chosen area of artificial intelligence and practical checking of this knowledge by working on individual project. The choice of programming language for the project is not limited, for recommended topics see PB016 Introduction to Artificial Intelligence.

Syllabus

• Study of a chosen area of artificial intelligence
• Project implementation.

Literature

• Russell,Stuart J. and Norvig, Peter: Artificial intelligence :a modern approach, 2nd edition, Upper Saddle River : Prentice Hall, 2003.
• NILSSON, Nils J. Artificial intelligence :a new synthesis. San Francisco: Morgan Kaufmann Publishers, 1998, xxi, 513 s. ISBN 1-55860-535-5. info
• NORVIG, Peter and Stuart Jonathan RUSSELL. Artificial intelligence :a modern approach. Upper Saddle River: Prentice Hall, 1995, xxviii, 93. ISBN 0-13-103805-2. info
• COHEN, Paul R. Empirical methods for artificial intelligence. Cambridge: MIT Press, 1995, xvi, 404. ISBN 0262032252. info

Teaching methods

Individual work on analysis and implementation of the project, preparation of documentation, with regular consultations with the lecturer.

Assessment methods

Consultations during the project work. Presentation of the implemented project, creation of HTML documentation of the project (see examples at the course web page).

Language of instruction

Czech

Further Comments

Study Materials
The course is taught annually.

Teacher's information

http://nlp.fi.muni.cz/uiprojekt/

• Enrolment Statistics (Spring 2014, recent)
  
• Permalink: https://is.muni.cz/course/fi/spring2014/PA026