PB016 Introduction to Artificial Intelligence

Faculty of Informatics
Autumn 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.
  • Natural language processing.
  • Learning, decision trees, neural networks.
  • Deep learning
  • 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.
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 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2024/PB016