PA026 Artificial Intelligence Project

Faculty of Informatics
Spring 2024
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).
Taught in person.
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
Mon 10:00–11:50 C416
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 53 fields of study the course is directly associated with, display
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.
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.
  • Study of a chosen area of artificial intelligence
  • Project implementation.
  • 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
Further Comments
Study Materials
The course is taught annually.
Teacher's information
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 2025.
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