PB016 Artificial Intelligence I

Faculty of Informatics
Autumn 2017
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).
Teacher(s)
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
Timetable
Tue 10:00–11:50 A318
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
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.
Syllabus
  • 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.
Literature
  • 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
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
http://nlp.fi.muni.cz/uui/
The course is also listed under the following terms Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Autumn 2017, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2017/PB016