FI:PB016 Intro to AI - Course Information

PB016 Introduction to Artificial Intelligence

Extent and Intensity

2/2/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Synchronous online teaching

Teacher(s)

doc. RNDr. Aleš Horák, Ph.D. (lecturer)
doc. Mgr. Bc. Vít Nováček, PhD (seminar tutor)
RNDr. Vladimír Míč, Ph.D. (seminar tutor)
Mgr. Filip Gregora (seminar tutor)
Mgr. Ondřej Huvar (seminar tutor)
Bc. Jindřich Matuška (seminar tutor)
Bc. Kryštof Matuštík (seminar tutor)
Bc. Matěj Pavlík (seminar tutor)
Bc. Filip Polák (seminar tutor)
Tomáš Cvejn (seminar tutor)
Matúš Čarnogurský (seminar tutor)
Bc. Martin Tvarožek (seminar tutor)
Bc. Pavol Trnavský (seminar tutor)
Tomáš Hutňan (seminar tutor)
Jakub Němec (seminar tutor)
Richard Wittner (seminar tutor)

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

Mon 14. 9. to Mon 14. 12. Mon 14:00–15:50 A217

• Timetable of Seminar Groups:

PB016/01: Wed 16. 9. to Wed 16. 12. Wed 12:00–13:50 C121, except Wed 30. 9., V. Míč
PB016/02: Fri 18. 9. to Fri 18. 12. Fri 10:00–11:50 S405, except Fri 2. 10., V. Míč
PB016/03: Mon 14. 9. to Mon 14. 12. Mon 10:00–11:50 C121, O. Huvar
PB016/04: Wed 16. 9. to Wed 16. 12. Wed 14:00–15:50 C119, except Wed 30. 9., F. Polák
PB016/05: Mon 14. 9. to Mon 14. 12. Mon 8:00–9:50 S405, J. Matuška
PB016/06: Wed 16. 9. to Wed 16. 12. Wed 8:00–9:50 S405, except Wed 30. 9., K. Matuštík
PB016/07: Tue 15. 9. to Tue 15. 12. Tue 10:00–11:50 C121, except Tue 29. 9., T. Hutňan
PB016/08: Mon 14. 9. to Mon 14. 12. Mon 16:00–17:50 C121, J. Němec
PB016/09: Mon 14. 9. to Mon 14. 12. Mon 8:00–9:50 C122, R. Wittner
PB016/10: Wed 16. 9. to Wed 16. 12. Wed 16:00–17:50 C121, except Wed 30. 9., J. Matuška
PB016/11: Wed 16. 9. to Wed 16. 12. Wed 10:00–11:50 C122, except Wed 30. 9., V. Nováček, rezervní skupina

Prerequisites

Basic knowledge of the Python programming language is expected, Python is used in the exercises.

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 40 fields of study the course is directly associated with, display

Abstract

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.

Key topics

• Artificial intelligence, Turing test, problem solving.
• Solving problems by searching.
• Problem decomposition, AND/OR graphs, Constraint Satisfaction Problems.
• Games and basic game strategies.
• Logic agents, propositional logic.
• First order predicate logic.
• Inference in propositional and predicate logic.
• Knowledge representation and reasoning, reasoning with uncertainty.
• Natural language processing.
• Learning, decision trees, neural networks.
• Deep learning.
• Generative models.

Study resources and literature

• Stuart Russel & Peter Norvig: Artificial intelligence : a modern approach, 4th ed., Pearson, 2020
• Dan Jurafsky and James H. Martin. Speech and Language Processing (2026, 3rd ed. draft). https://web.stanford.edu/~jurafsky/slp3/
• Sylaby přednášek.

Approaches, practices, and methods used in teaching

Lectures and exercises.

Method of verifying learning outcomes and course completion requirements

The final grade consists of tests during the exercises, a written midterm exam and a written final exam.

Language of instruction

Czech

Follow-Up Courses

• PA026 Artificial Intelligence Project

Teacher's information

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

Further Comments

Study Materials
The course is taught annually.

Listed among pre-requisites of other courses

• PA026 Artificial Intelligence Project
  PB016 || IV126 || PV021 || PV056  

• Enrolment Statistics (recent)
  
• Permalink: https://is.muni.cz/course/fi/autumn2026/PB016