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|[[Maria Markosova|Mária Markošová]],  [[Igor Farkas|Igor Farkaš]]
|[[Maria Markosova|Mária Markošová]],  [[Igor Farkas|Igor Farkaš]]
|Exercises (other students)
|[[Peter Gergel|Peter Gergeľ]]
|Exercises (students of m/AIN, m/INF)
|Exercises (students of m/AIN, m/INF)
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== Course grading ==
== Course grading ==
* Exercises (30%).
* Exercises (25%).
* Final exam (50%).
* Final exam (50%).
* Projects (20%)
* 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

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.


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


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


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).