PA026 Artificial Intelligence Project

Faculty of Informatics
Spring 2026
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
In-person direct teaching
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
Contact Person: doc. RNDr. Aleš Horák, Ph.D.
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Wed 18. 2. to Wed 13. 5. Wed 14:00–15:50 A220
Prerequisites
PB016 Intro to AI || IV126 Fundamentals of AI || PV021 Neural Networks || PV056 ML and Data Mining
This course is given in English. Presentations and project documentation can be in English, Czech or Slovak.
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 32 fields of study the course is directly associated with, display
Abstract
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.
Learning outcomes
Students will be able to:
- design, analyze and elaborate a solution of a selected task in the field of artificial intelligence;
- present the selected step-by-step approach;
- justify the chosen implementation process;
- design an evaluation process of the created application and process its results.
Key topics
  • Study of a chosen area of artificial intelligence
  • Project implementation.
Study resources and literature
  • Stuart Russel & Peter Norvig: Artificial intelligence : a modern approach, 4th ed., Prentice Hall, 2020.
  • Sutton and Barto. Reinforcement Learning: An Introduction, 2nd edition, MIT Press, 2017.
  • Hector Cuesta: Practical Data Analysis, Packt Publishing, 2013. 360 s., ISBN: 1-78328-099-9.
Approaches, practices, and methods used in teaching
Individual work on analysis and implementation of the project, preparation of documentation, with regular consultations with the lecturer.
Method of verifying learning outcomes and course completion requirements
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
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://nlp.fi.muni.cz/aiproject/
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/spring2026/PA026