Logo ČVUT
ČESKÉ VYSOKÉ UČENÍ TECHNICKÉ V PRAZE
STUDIJNÍ PLÁNY
2023/2024
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

Artificial Intelligence Fundamentals

Předmět není vypsán Nerozvrhuje se
Kód Zakončení Kredity Rozsah Jazyk výuky
BIE-ZUM Z,ZK 4 2P+2C anglicky
Garant předmětu:
Pavel Surynek
Přednášející:
Pavel Surynek
Cvičící:
Pavel Surynek
Předmět zajišťuje:
katedra aplikované matematiky
Anotace:

Students are introduced to the fundamental problems in the Artificial Intelligence, and the basic methods for their solving. It focuses mainly on the classical tasks from the areas of state space search, multi-agent systems, game theory, planning, and machine learning. Modern soft-computing methods, including the evolutionary algorithms and the neural networks, will be presented as well.

Požadavky:

Basic knowledge of statistics and algebra. Programming capabilities.

Osnova přednášek:

1. Introduction to Artiffcial Intelligence and its history. Turing test, rational behavior and reasoning.

2. The state space and the heuristic methods for state space exploration.

3. Advanced state space search methods: Hill climbing, Simulated annealing, tabu search, population-based methods.

4. Evolutionary computation techniques. Genetic algorithm, operators of initialization, crossover, mutation, and reproduction.

5. Genetic programming, evolution of tree structures. Crossover and mutation of subtrees.

6. Constraint satisfaction problems and the heuristics for their solving.

7. Automated planning. Planning state space search, plans, and actions. Relaxation and abstraction in planning.

8. Multi-agent system and their architectures. Relations between the world and the agents, agent types, utility functions.

9. Game theory. Games in the normal form, game analysis. Pareto-optimality, Nash equilibrium.

10. Game in the extensive form, methods for searching the game tree. Minimax algorithm, alpha-beta pruning.

11. Introduction to Machine learning and Data mining. Supervised and unsupervised learning. Classification, regression, and cluster analysis.

12. Artificial neural networks. Perceptron networks, activation function, backpropagation algorithm, self-organizing networks.

13. Other computational intelligence methods, modern trends.

Osnova cvičení:

1. Interactive tools for artificial intelligence

2. AI problem set 1

3. AI problem set 2

4. Programming assignment 1

5. Consulting assignment 1

6. AI problem set 3

7. AI problem set 4

8. Programming assignment 2

9. Consulting assignment 2

10. AI problem set 5

11. Programming assignment 3

12. Consulting assignment 3

13. Reserved, credit

Cíle studia:

The course aims to offer students a survey to the areas of Artificial Intelligence. Its main objective is to present a comprehensive overview of AI problems, rather than examining individual methods in detail.

Studijní materiály:

S. Russell, P. Norvig: „Artificial Intelligence: A Modern Approach (Third Edition)“. ISBN: 978-0136042594. Prentice Hall, 2009.

Poznámka:

Information about the course and courseware are available at https://courses.fit.cvut.cz/BI-ZUM/

Další informace:
https://courses.fit.cvut.cz/BI-ZUM/
Pro tento předmět se rozvrh nepřipravuje
Předmět je součástí následujících studijních plánů:
Platnost dat k 27. 3. 2024
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/cs/predmet2358306.html