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, project assignments, and lecture slides, please see LIST.
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|Lecture||Thursday||11:30||M-X||Mária Markošová, Ľubica Beňušková|
|27.09.||What is artificial intelligence, properties and types of agents. Uninformed search - state space, uninformed search algorithms, DFS, BFS.||R&N, ch.2-3.4|
|04.10.||Informed search, A* algorithm, heuristics and their properties.||R&N, ch.3.5-3.6|
|11.10.||Constraint satisfaction problem: definition, heuristics, methods of solving.||R&N, ch.6|
|18.10.||Local search, looking for an optimum, hill climbing, genetic algorithm, simulated annealing etc.||R&N, ch.4.1|
|25.10.||Basics of game theory, MiniMax algorithm, Alpha-Beta pruning, ExpectiMiniMax.||R&N, ch.5|
|08.11.||Logical agents: inference in logical knowledge base.||R&N, ch.7|
|15.11.||Supervised learning: linear and non-linear regression, binary perceptron.||R&N, ch.18.1-18.2, 18.6-18.6.3|
|22.11.||Multi-layer perceptron, idea of error backpropagation.||R&N, ch.18.6.4-18.7.5|
|29.11.||Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet|
|06.12.||Unsupervised learning: K-means clustering, KNN, Self-organizing map, Principal component analysis||R&N, ch.18.8-18.8.2|
|13.12.||Weight optimization of MLP using genetic algorithms, evolutionary robotics|
|20.12.||Quo vadis AI? Problems and visions of future AI methods||R&N, ch. 26|
- Russell S., Norwig P. (2010). Artificial Intelligence: A Modern Approach, (3rd ed.), Prentice Hall. Available in the faculty library. A.k.a. AIMA.
- Marsland S. (2015). Machine Learning: An Algorithmic Perspective, (2nd ed.), CRC Press.
- Zadeh L. (2007). Fuzzy logic, Scholarpedia, 3(3):1766.
The course grading consists of four parts:
- Exercises (25%)
- Short tests (10%)
- Project (15%)
- Final exam (50%)
You have to gain at least half of the points from each of these in order to pass the course. (E.g. 10p from excercises + full from tests, project, and final exam will still result in Fx.)
Overall grading: A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0).