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Revision as of 17:09, 27 September 2017
Introduction to Artificial Intelligence 1-AIN-304
Contents
The course objectives are to provide the students with basic insight into artificial intelligence, that can further be extended in the master programmes. The course covers the basics of symbolic and nature-inspired artificial intelligence. The theory is combined with practical exercises.
Course schedule
Type | Day | Time | Room | Lecturer |
---|---|---|---|---|
Lecture | Thursday | 11:30 | M-IV | Mária Markošová, Igor Farkaš |
Exercises (students of mIKV) | Thursday | 16:30 | H-6 | Peter Gergeľ |
Exercises (other students) | Monday (next week) | 9:50 | H-6 | Juraj Holas |
Syllabus
Date | Topic | References |
---|---|---|
28.09. | What is artificial intelligence, agent-robot going around an obstacle, properties and types of agents. | todo R&N, chap.X |
05.10. | Search, state space, tree search, searching agent, uninformed search. | todo |
12.10. | informed search, graph search versus tree search, methods of implementation, heuristics and their properties. | R&N (2010), todo |
19.10. | Constraint satisfaction problem: definition, heuristics, methods of solving. | R&N (2010), ch.X |
26.10. | Basics of game theory, minimax algorithm. | todo |
02.11. | More complex agents: making inferences and learning. Propositional logic, making inferences. | todo |
09.11. | Learning from examples: supervised learning, perceptron, classification, regression, model selection, generalization, regularization. | R&N, ch.18.x |
16.11. | Nonparametric models, methods of nearest neighobors, finding nearest neighbors with k-d trees, regression. | R&N, ch.18.y |
23.11. | Probabilistic computation: basic concepts and methods (Bayes formula). | R&N, ch.13,20 |
30.11. | Reinforcement learning, basic concepts, methods of learning. | R&N, ch.21.3-5. |
07.12. | Fuzzy systems, fuzzy logic and reasoning. | Zadeh (2007) |
07.12. | Robotics: basic concepts and tasks. | R&N (2010), ch.25 |
References
- Russell S., Norwig P. (2010). Artificial Intelligence: A Modern Approach, (3rd ed.). Available in the faculty library.
- Zadeh L. (2007). Fuzzy logic, Scholarpedia, 3(3):1766.
Course grading
- Exercises (30%).
- Final exam (50%).
- Projects (20%)
- Overall grading: A (50-46), B (45-41), C (40-36), D (35-31), E (30-26), Fx (25-0).