FI:PB016 Artificial Intelligence I - Course Information
PB016 Artificial Intelligence IFaculty of Informatics
- Extent and Intensity
- 2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
- doc. RNDr. Aleš Horák, Ph.D. (lecturer)
- Guaranteed by
- doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing - Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing - Faculty of Informatics
- Wed 12:00–13:50 A217
- 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 the course is directly associated with
- there are 24 fields of study the course is directly associated with, display
- Course objectives
- Introduction to problem solving in the area of artificial intelligence. The main aim of the course is to provide information about fundamental algorithms used in AI.
- Learning outcomes
- After studying the course, the students will be able to:
- identify and summarize tasks related to the field of artificial intelligence;
- compare and describe basic search space algorithms;
- compare and describe main aspects of logical systems;
- understand different approaches to machine learning;
- compare and describe different ways of knowledge representation and reasoning;
- present basic approaches to computer processing of natural languages.
- The Prolog language.
- Operations and data structures.
- State space searching.
- Heuristics, Best-first search, A* search.
- Problem decomposition, AND/OR graphs.
- Constraint Satisfaction Problems.
- Games and basic game strategies.
- Intelligent agents, propositional logic, first order predicate logic.
- TIL - transparent intensional logic.
- Knowledge representation and reasoning.
- Learning, decision trees, neural networks.
- Deep Learning Applications
- Natural language processing.
- Stuart Russel \& Peter Norvig: Artificial intelligence : a modern approach, 2nd.ed., Prentice Hall, 2003.
- BRATKO, Ivan. Prolog programming for artificial intelligence. 3rd ed. Harlow: Addison-Wesley, 2001. xxi, 678 s. ISBN 0-201-40375-7. 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
- Sylaby přednášek.
- Teaching methods
- Lectures with recommended self-study of examples, with voluntary student talks.
- Assessment methods
- The final grade consists of 2 written tests and voluntary student presentations.
- Language of instruction
- Follow-Up Courses
- Further Comments
- Study Materials
The course is taught annually.
- Listed among pre-requisites of other courses
- Teacher's information