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 methods of artificial intelligence. The theory is combined with practical exercises.
For exercises, please see LIST.
Course schedule
Type | Day | Time | Room | Lecturer |
---|---|---|---|---|
Lecture | Thursday | 11:30 | M-IV | Mária Markošová, Igor Farkaš |
Exercises (other students) | Thursday | 16:30 | I-23 | Peter Gergeľ |
Exercises (students of m/AIN, m/INF) | 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. | R&N, ch.2 |
05.10. | Search, state space, tree search, searching agent, uninformed search. | R&N,ch.3.1-4 |
12.10. | Informed search, graph search versus tree search, methods of implementation, heuristics and their properties. | R&N, ch.3.5-6 |
19.10. | Constraint satisfaction problem: definition, heuristics, methods of solving. | R&N, ch.6 |
26.10. | Basics of game theory, minimax algorithm. | R&N, ch.5 |
02.11. | More complex agents: making inferences and learning. Propositional logic, making inferences. | R&N, ch.7 |
09.11. | Learning from examples: supervised learning, perceptron, classification, regression, model selection, generalization. | R&N, ch.18.1-7 |
16.11. | Unsupervised learning in neural networks: Principal component analysis, Self-organizing map. | Marsland (2015), ch.6 & 14 |
23.11. | Probabilistic learning: basic concepts and methods (Bayes formula). | R&N, ch.13,20 |
30.11. | Reinforcement learning, basic concepts, methods of learning. | R&N, ch.21 |
07.12. | Fuzzy systems, fuzzy logic and reasoning. | Zadeh (2007) |
14.12. | Robotics: basic concepts and tasks. | R&N, ch.25 |
References
- Russell S., Norwig P. (2010). Artificial Intelligence: A Modern Approach, (3rd ed.), Prentice Hall. Available in the faculty library.
- Marsland S. (2015). Machine Learning: An Algorithmic Perspective, (2nd ed.), CRC Press.
- 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).