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A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

BI-ZUM Artificial Intelligence Fundamentals Extent of teaching: 2P+2C
Instructor: Surynek P. Completion: Z,ZK
Department: 18105 Credits: 4 Semester: L

Annotation:
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.

Lecture syllabus:
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.

Seminar syllabus:
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

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

Requirements:
Basic knowledge of statistics, algebra and algorithmization. Programming capabilities.

Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/BI-ZUM/

The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester
BI-WSI-WI.2015 Web and Software Engineering V 4
BI-WSI-PG.2015 Web and Software Engineering V 4
BI-WSI-SI.2015 Web and Software Engineering V 4
BI-SPOL.2015 Unspecified Branch/Specialisation of Study VO 4
BI-BIT.2015 Computer Security and Information technology V 4
BI-TI.2015 Computer Science V 4
BI-BIT.2015 Computer Security and Information technology V 4
BI-PI.2015 Computer engineering V 4
BI-ZI.2018 Knowledge Engineering PO 4
BI-ISM.2015 Information Systems and Management V 4


Page updated 28. 3. 2024, semester: Z/2023-4, L/2019-20, L/2022-3, Z/2019-20, Z/2022-3, L/2020-1, L/2023-4, Z/2020-1, Z,L/2021-2, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška