Artificial Intelligence Fundamentals
- Department of Applied Mathematics
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.
Basic knowledge of statistics, algebra and algorithmization. Programming capabilities.
- Syllabus of lectures:
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.
- Syllabus of tutorials:
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
- Study Objective:
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.
- Study materials:
S. Russell, P. Norvig: „Artificial Intelligence: A Modern Approach (Third Edition)“. ISBN: 978-0136042594. Prentice Hall, 2009.
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans:
- Information Technology - Version for those who Enrolled in 2014 (in Czech) (elective course)
- Information Systems and Management - Version for those who Enrolled in 2014 (in Czech) (elective course)
- Bc. Programme Informatics, in Czech, Version 2015 to 2019 (VO)
- Bc. Branch Security and Information Technology, in Czech, Version 2015 to 2019 (elective course)
- Bc. Branch Computer Science, in Czech, Version 2015 to 2019 (elective course)
- Bc. Branch Computer Engineering, in Czech, Version 2015 to 2019 (elective course)
- Bachelor Branch Information Systems and Management, in Czech, Version 2015 to 2019 (elective course)
- Bachelor Branch Knowledge Engineering, in Czech, Version 2015, 2016 and 2017 (compulsory course of the specialization)
- Bachelor Branch WSI, Specialization Software Engineering, in Czech, Version 2015 to 2019 (elective course)
- Bachelor Branch, Specialization Web Engineering, in Czech, Version 2015 to 2019 (elective course)
- Bachelor Branch WSI, Specialization Computer Grafics, in Czech, Version 2015 to 2019 (elective course)
- Bachelor Branch Knowledge Engineering, in Czech, Version 2018 to 2019 (compulsory course of the specialization)