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Introduction to Artificial Intelligence - NAIL113

Title:	Úvod do umělé inteligence
Guaranteed by:	Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2020
Semester:	summer
E-Credits:	5
Hours per week, examination:	summer s.:2/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	not taught
Language:	Czech, English
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	prof. RNDr. Roman Barták, Ph.D.

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Annotation -

Last update: doc. RNDr. Pavel Töpfer, CSc. (30.01.2018)

An introductory course covering basic concepts and methods of artificial intelligence. The course assumes knowledge of logic and probability theory at the undergraduate level.

Course completion requirements -

Last update: doc. RNDr. Pavel Töpfer, CSc. (30.01.2018)

In order to pass the course, the student must obtain the credit for the seminar and pass an exam. The credit is given for solving assignments from the seminar. The nature of study verification excludes the possibility of its repetition. The exam is oral with time for written preparation. The requirements correspond to the syllabus in the extent presented during the lectures. A part of the exam may be the design of an algorithm for a given problem.

Literature -

Last update: doc. RNDr. Pavel Töpfer, CSc. (30.01.2018)

S. Russell, P. Norvig: Artificial Intelligence; A Modern Approach, 2010

V. Mařík, O. Štepánková, J. Lažanský a kol.: Umělá Inteligence, 1-6. Academia, Praha

Syllabus -

Last update: doc. RNDr. Pavel Töpfer, CSc. (30.01.2018)

1. Basic terminology, history, background

2. Problem solving via search (A* and others)

3. Constraint satisfaction

4. Logical reasoning (forward and backward chaining, resolution, SAT)

5. Probabilistic reasoning (Bayesian networks)

6. Knowledge representation (situation calculus, Markovian models)

7. Automated planning

8. Markov decision processes

9. Games and theory of games

10. Machine learning (decision trees, regression, reinforcement learning)

11. Philosophical and ethical aspects