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, project assignments, and lecture slides, please see the course in moodle.
Course schedule
| Type | Day | Time | Room | Lecturer |
|---|---|---|---|---|
| Lecture | Monday | 16:30 | A | Mária Markošová, Ľubica Beňušková |
| Exercises | Tuesday | 18:10 | F2-295 (I-H6) | Štefan Pócoš, Iveta Bečková |
Lecture syllabus
| Date | Topic | References |
|---|---|---|
| 18.9. | What is artificial intelligence, properties and types of agents. Uninformed search - state space, uninformed search algorithms, DFS, BFS. | R&N, ch.2-3.4 |
| 25.9. | Informed search, A* algorithm, heuristics and their properties. | R&N, ch.3.5-3.6 |
| 2.10. | Local search, looking for an optimum, hill climbing, genetic algorithm, simulated annealing etc. | R&N, ch.4.1 |
| 9.10. | Constraint satisfaction problem: definition, heuristics, methods of solving. | R&N, ch.6 |
| 16.10. | Basics of game theory, MiniMax algorithm, Alpha-Beta pruning, ExpectiMiniMax. | R&N, ch.5 |
| 23.10. | Logical agents: inference in logical knowledge base. | R&N, ch.7 |
| 30.10. | Holiday - Sviatok všetkých svätých | |
| 6.11. | Introduction to nature-inspired computing, inductive learning, regression. | R&N, ch.18.1-18.2, 18.6.1-18.6.2 |
| 13.11. | Single perceptron, delta rule. | R&N, ch.18.6.3-18.6.4 |
| 20.11. | Multi-layer perceptron, error backpropagation. | R&N, ch.18.7 |
| 27.11. | Unsupervised learning: K-means clustering, KNN, Self-organizing map. | R&N, ch.18.8-18.8.2 |
| 4.12. | Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet | Artificial Neural Networks |
| 11.12. | Quo vadis AI? Problems and visions of future AI methods. | R&N, ch. 26 |
References
- Russell S., Norwig P., Artificial Intelligence: A Modern Approach (3rd ed.), Prentice Hall, 2010. Ask lecturers for password. Also available in the faculty library. A.k.a. AIMA.
- Návrat P., Bieliková M., Beňušková Ľ. et al., Umelá inteligencia (3. vydanie), Vydavateľstvo STU, 2015. (ANN chapter)
Course grading
The course grading consists of three parts:
- Exercises (36%)
- Project (14%)
- Final exam (50%)
Throughout the semester, you can gain 36% for exercises and 14% for the project. You have to earn at least half from each of these. If you do not meet minimal condition from the semester, then you cannot pass the exam. The final exam is worth 50% of the total mark.
Overall grading: A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0).