FI:PB016 Intro to AI - Course Information
PB016 Introduction to Artificial Intelligence
Faculty of InformaticsAutumn 2024
- 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).
Taught in person. - Teacher(s)
- doc. RNDr. Aleš Horák, Ph.D. (lecturer)
doc. Mgr. Bc. Vít Nováček, PhD (seminar tutor)
Adam Džadoň (seminar tutor)
Filip Gregora (seminar tutor)
Bc. Kristína Hanicová (seminar tutor)
Bc. Ondřej Huvar (seminar tutor)
Mgr. Daniel Iľkovič (seminar tutor)
Michal Jakubík (seminar tutor)
Bc. Ondřej Metelka (seminar tutor)
Bc. Terézia Mikulová (seminar tutor)
Bc. Matěj Pavlík (seminar tutor)
Mgr. Bc. Roman Solař (seminar tutor)
Mgr. et Mgr. Matúš Šikyňa (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 - 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 39 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
- 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, satisfiability.
- Truth and provability. Axiomatic systems.
- First order predicate logic, intensional logic.
- Resolution in propositional and predicate logic. Introduction to logic programming.
- Knowledge representation and reasoning, reasoning with uncertainty.
- Learning, decision trees, neural networks.
- Natural language processing.
- Generative models
- Literature
- Stuart Russel & Peter Norvig: Artificial intelligence : a modern approach, 4th ed., Pearson, 2020
- Sylaby přednášek.
- Teaching methods
- Lectures and exercises.
- Assessment methods
- 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
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
The course is taught: every week. - Listed among pre-requisites of other courses
- Teacher's information
- http://nlp.fi.muni.cz/uui/
New seminar groups will be added, and each enrolled student will be able to join the exercises. If you need an English group, join/ask for joining to PB016/01.
- Enrolment Statistics (Autumn 2024, recent)
- Permalink: https://is.muni.cz/course/fi/autumn2024/PB016