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<!--[https://sluzby.fmph.uniba.sk/infolist/sk/1-AIN-304_15.html Informačný list predmetu]--> | <!--[https://sluzby.fmph.uniba.sk/infolist/sk/1-AIN-304_15.html Informačný list predmetu]--> | ||
− | For exercises, project assignments, and lecture slides, please see [https:// | + | For exercises, project assignments, and lecture slides, please see the course in [https://moodle.uniba.sk/course/view.php?id=3062 moodle]. |
+ | |||
− | |||
− | |||
== Course schedule == | == Course schedule == | ||
Riadok 22: | Riadok 21: | ||
|- | |- | ||
|Lecture | |Lecture | ||
− | | | + | |Monday |
− | | | + | |16:30 |
|M-X | |M-X | ||
|[[Maria Markosova|Mária Markošová]], [[Lubica Benuskova|Ľubica Beňušková]] | |[[Maria Markosova|Mária Markošová]], [[Lubica Benuskova|Ľubica Beňušková]] | ||
|- | |- | ||
|Exercises | |Exercises | ||
− | | | + | |Tuesday |
− | | | + | |18:10 |
− | |I- | + | |F2-295 (I-H6) |
− | |[[ | + | |[[Stefan Pocos|Štefan Pócoš]], [[Iveta Beckova|Iveta Bečková]] |
|} | |} | ||
Riadok 43: | Riadok 42: | ||
!References | !References | ||
|- | |- | ||
− | | | + | |18.9. |
|What is artificial intelligence, properties and types of agents. Uninformed search - state space, uninformed search algorithms, DFS, BFS. | |What is artificial intelligence, properties and types of agents. Uninformed search - state space, uninformed search algorithms, DFS, BFS. | ||
|R&N, ch.2-3.4 | |R&N, ch.2-3.4 | ||
|- | |- | ||
− | | | + | |25.9. |
|Informed search, A* algorithm, heuristics and their properties. | |Informed search, A* algorithm, heuristics and their properties. | ||
|R&N, ch.3.5-3.6 | |R&N, ch.3.5-3.6 | ||
|- | |- | ||
− | | | + | |2.10. |
− | + | ||
− | + | ||
− | + | ||
− | + | ||
|Local search, looking for an optimum, hill climbing, genetic algorithm, simulated annealing etc. | |Local search, looking for an optimum, hill climbing, genetic algorithm, simulated annealing etc. | ||
|R&N, ch.4.1 | |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. | |Basics of game theory, MiniMax algorithm, Alpha-Beta pruning, ExpectiMiniMax. | ||
|R&N, ch.5 | |R&N, ch.5 | ||
|- | |- | ||
− | | | + | |23.10. |
|Logical agents: inference in logical knowledge base. | |Logical agents: inference in logical knowledge base. | ||
|R&N, ch.7 | |R&N, ch.7 | ||
|- | |- | ||
− | | | + | |30.10. |
− | | | + | |Introduction to nature-inspired computing, inductive learning, regression. |
− | |R&N, ch.18.1-18.2, 18.6-18.6. | + | |R&N, ch.18.1-18.2, 18.6.1-18.6.2 |
|- | |- | ||
− | | | + | |6.11. |
− | | | + | |Single perceptron, delta rule. |
− | |R&N, ch.18.6. | + | |R&N, ch.18.6.3-18.6.4 |
|- | |- | ||
− | | | + | |13.11. |
− | | | + | |Multi-layer perceptron, error backpropagation. |
− | | | + | |R&N, ch.18.7 |
|- | |- | ||
− | | | + | |20.11. |
− | |Unsupervised learning: K-means clustering, KNN, Self-organizing map | + | |Unsupervised learning: K-means clustering, KNN, Self-organizing map. |
|R&N, ch.18.8-18.8.2 | |R&N, ch.18.8-18.8.2 | ||
|- | |- | ||
− | | | + | |27.11. |
− | | | + | |Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet |
− | | | + | |[http://cogsci.fmph.uniba.sk/~holas/res/UI-ch6-ANN.pdf Artificial Neural Networks] |
+ | |- | ||
+ | |4.12. | ||
+ | |Evolutionary robotics | ||
+ | |R&N, ch. 25.1.-25.2, 25.7 | ||
|- | |- | ||
− | | | + | |11.12. |
− | |Quo vadis AI? Problems and visions of future AI methods | + | |Quo vadis AI? Problems and visions of future AI methods. |
|R&N, ch. 26 | |R&N, ch. 26 | ||
|} | |} | ||
Riadok 95: | Riadok 98: | ||
− | * Russell S., Norwig P. | + | * Russell S., Norwig P., [http://cogsci.fmph.uniba.sk/~holas/res/AIMA3.pdf Artificial Intelligence: A Modern Approach (3rd ed.)], Prentice Hall, 2010. Ask lecturers for password. Also available in the faculty library. A.k.a. ''AIMA''.<!-- user:uui, password:uui --> |
− | * | + | * Návrat P., Bieliková M., Beňušková Ľ. et al., [https://www.martinus.sk/?uItem=224899 Umelá inteligencia (3. vydanie)], Vydavateľstvo STU, 2015. ([http://cogsci.fmph.uniba.sk/~holas/res/UI-ch6-ANN.pdf ANN chapter]) |
− | + | ||
== Course grading == | == Course grading == | ||
− | The course grading consists of | + | The course grading consists of three parts: |
− | * Exercises ( | + | * Exercises (36%) |
− | + | * Project (14%) | |
− | * Project ( | + | |
* Final exam (50%) | * Final exam (50%) | ||
− | You have to | + | 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). | '''Overall grading:''' A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0). |
Verzia zo dňa a času 15:28, 26. október 2023
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, project assignments, and lecture slides, please see the course in moodle.
Course schedule
Type | Day | Time | Room | Lecturer |
---|---|---|---|---|
Lecture | Monday | 16:30 | M-X | 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. | Introduction to nature-inspired computing, inductive learning, regression. | R&N, ch.18.1-18.2, 18.6.1-18.6.2 |
6.11. | Single perceptron, delta rule. | R&N, ch.18.6.3-18.6.4 |
13.11. | Multi-layer perceptron, error backpropagation. | R&N, ch.18.7 |
20.11. | Unsupervised learning: K-means clustering, KNN, Self-organizing map. | R&N, ch.18.8-18.8.2 |
27.11. | Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet | Artificial Neural Networks |
4.12. | Evolutionary robotics | R&N, ch. 25.1.-25.2, 25.7 |
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).