SubjectsSubjects(version: 845)
Course, academic year 2018/2019
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Introduction to Artificial Intelligence - NAIL120
Title in English: Úvod do umělé inteligence
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/2 C+Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: not taught
Language: Czech, English
Teaching methods: full-time
Guarantor: prof. RNDr. Roman Barták, Ph.D.
Class: Informatika Bc.
Classification: Informatics > Informatics, Software Applications, Computer Graphics and Geometry, Database Systems, Didactics of Informatics, Discrete Mathematics, External Subjects, General Subjects, Computer and Formal Linguistics, Optimalization, Programming, Software Engineering, Theoretical Computer Science
Annotation -
Last update: RNDr. Jan Hric (30.04.2018)
An introductory course covering basic concepts and methods of artificial intelligence. The course assumes knowledge of logic and probability theory at the undergraduate level.
Aim of the course -
Last update: RNDr. Jan Hric (30.04.2018)

The aim of the course is to give students an overview of fundamental methods and concepts of artificial intelligence and to make students familiar with practical usage of them.

Course completion requirements -
Last update: RNDr. Jan Hric (30.04.2018)

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: RNDr. Jan Hric (30.04.2018)

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

Syllabus -
Last update: RNDr. Jan Hric (30.04.2018)

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

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