| Riadok 8: | Riadok 8: | ||
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| − | For exercises, please see | + | For exercises, please see [http://list.fmph.uniba.sk LIST]. |
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| Riadok 28: | Riadok 28: | ||
|[[Maria Markosova|Mária Markošová]], [[Igor Farkas|Igor Farkaš]] | |[[Maria Markosova|Mária Markošová]], [[Igor Farkas|Igor Farkaš]] | ||
|- | |- | ||
| − | |Exercises (students | + | |Exercises (other students) |
|Thursday | |Thursday | ||
|16:30 | |16:30 | ||
| − | | | + | |I-23 |
|[[Peter Gergel|Peter Gergeľ]] | |[[Peter Gergel|Peter Gergeľ]] | ||
|- | |- | ||
| − | |Exercises ( | + | |Exercises (students of m/AIN, m/INF) |
|Monday (next week) | |Monday (next week) | ||
|9:50 | |9:50 | ||
| Riadok 52: | Riadok 52: | ||
|28.09. | |28.09. | ||
|What is artificial intelligence, agent-robot going around an obstacle, properties and types of agents. | |What is artificial intelligence, agent-robot going around an obstacle, properties and types of agents. | ||
| − | |R&N, | + | |R&N, ch.2 |
|- | |- | ||
|05.10. | |05.10. | ||
| Riadok 75: | Riadok 75: | ||
|- | |- | ||
|09.11. | |09.11. | ||
| − | |Learning from examples: supervised learning, perceptron, classification, regression, model selection, generalization | + | |Learning from examples: supervised learning, perceptron, classification, regression, model selection, generalization. |
|R&N, ch.18.x | |R&N, ch.18.x | ||
|- | |- | ||
Verzia zo dňa a času 07:55, 28. september 2017
Introduction to Artificial Intelligence 1-AIN-304
Obsah
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.X |
| 12.10. | Informed search, graph search versus tree search, methods of implementation, heuristics and their properties. | R&N, ch.X |
| 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.X |
| 02.11. | More complex agents: making inferences and learning. Propositional logic, making inferences. | R&N, ch.X |
| 09.11. | Learning from examples: supervised learning, perceptron, classification, regression, model selection, generalization. | 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 |
| 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.). 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).