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|[[Maria Markosova|Mária Markošová]], [[Igor Farkas|Igor Farkaš]] | |[[Maria Markosova|Mária Markošová]], [[Igor Farkas|Igor Farkaš]] | ||
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== Course grading == | == Course grading == | ||
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* Final exam (50%). | * Final exam (50%). | ||
− | * Projects ( | + | * Projects (25%) |
* <b>Overall grading:</b> A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0). | * <b>Overall grading:</b> A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0). |
Revision as of 12:11, 18 September 2018
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.
News
Exam (test): Thursday, 18. 1. 2018 at 9:00. Please sign up.
Course schedule
Type | Day | Time | Room | Lecturer |
---|---|---|---|---|
Lecture | Thursday | 11:30 | M-IV | Mária Markošová, Igor Farkaš |
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, informed search, graph search versus tree search, heuristics and their properties. | R&N,ch.3.1-4 |
12.10. | Local search search, looking for an optimum, hill climbing, genetic algorithm, simulated annealing etc. | 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, alpha beta pruning, expectiminimax. | R&N, ch.5 |
02.11. | More complex logical agents: inference in logical knowledge base. | 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 (25%).
- Final exam (50%).
- Projects (25%)
- Overall grading: A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0).