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| − | For exercises, please see [ | + | For exercises, project assignments, and lecture slides, please see the course in [https://moodle.uniba.sk/course/view.php?id=3062 moodle]. |
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== Course schedule == | == Course schedule == | ||
| Riadok 23: | Riadok 21: | ||
|- | |- | ||
|Lecture | |Lecture | ||
| − | | | + | |Monday |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
|16:30 | |16:30 | ||
| − | | | + | |A |
| − | |[[ | + | |[[Lubica Benuskova|Ľubica Beňušková]] |
|- | |- | ||
| − | |Exercises ( | + | |Exercises (DAV) |
| − | | | + | |Wednesday |
| + | |18:10 | ||
| + | |I-H3 | ||
| + | |[[Stefan Pocos|Štefan Pócoš]], [[Iveta Beckova|Iveta Bečková]], Branislav Zigo | ||
| + | |- | ||
| + | |Exercises (AIN) | ||
| + | |Friday | ||
|9:50 | |9:50 | ||
| − | | | + | |I-H6 |
| − | |[[ | + | |[[Stefan Pocos|Štefan Pócoš]], [[Iveta Beckova|Iveta Bečková]], Branislav Zigo |
|} | |} | ||
| − | == | + | == Lecture syllabus == |
{| class="alternative table-responsive" | {| class="alternative table-responsive" | ||
| Riadok 50: | Riadok 48: | ||
!References | !References | ||
|- | |- | ||
| − | | | + | |23.9. |
| − | | | + | |Introduction, basic notions for intelligent agents, reflex agents, reflex agents with a state |
| − | |R&N, ch.2 | + | |R&N, ch. 2 |
|- | |- | ||
| − | | | + | |30.9. |
| − | |Search, | + | |Search: uninformed, informed, uniform cost, DBS, BFS, A*, Min Max, greedy, alpha-beta, adversarial |
| − | |R&N,ch.3.1- | + | |R&N, ch. 3.2.2 -3.5.2, ch. 5.1-5.3 |
|- | |- | ||
| − | | | + | |7.10. |
| − | | | + | |Constraint Satisfaction Problems (CSP), heuristics, methods of solving |
| − | |R&N, ch. | + | |R&N, ch. 6.1 - 6.3.2 |
|- | |- | ||
| − | | | + | |14.10. |
| − | | | + | |Logical agents: inference in logical knowledge base |
| − | |R&N, ch. | + | |R&N, ch. 7.1 - 7.5, 9.3 - 9.4 |
|- | |- | ||
| − | | | + | |21.10. |
| − | | | + | |Local and global optimization, hill climbing, GA, simulated annealing |
| − | |R&N, ch. | + | |R&N, ch. 4.1 - 4.2 |
|- | |- | ||
| − | | | + | |28.10. |
| − | | | + | |Introduction to probability, reasoning with uncertainty, Bayes' rule |
| − | |R&N, ch. | + | |R&N, ch. 13.1 -13.5 |
|- | |- | ||
| − | | | + | |4.11. |
| − | | | + | |Introduction to nature-inspired computing, inductive learning, regression |
| − | |R&N, ch.18.1- | + | |R&N, ch.18.1-18.2, 18.6.1-18.6.2 |
|- | |- | ||
| − | | | + | |11.11. |
| − | | | + | |Single perceptron, delta rule for supervised learning |
| − | | | + | |R&N, ch.18.6.3-18.6.4 |
|- | |- | ||
| − | | | + | |18.11. |
| − | | | + | |Multi-layer perceptron, supervised learning by error backpropagation |
| − | |R&N, ch. | + | |R&N, ch.18.7 |
|- | |- | ||
| − | | | + | |25.11. |
| − | | | + | |Unsupervised learning: K-means clustering, KNN, Self-organizing map |
| − | |R&N, ch. | + | |R&N, ch.18.8-18.8.2 |
|- | |- | ||
| − | | | + | |2.12. |
| − | | | + | |Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet |
| − | | | + | |[http://cogsci.fmph.uniba.sk/~holas/res/UI-ch6-ANN.pdf Artificial Neural Networks] |
|- | |- | ||
| − | | | + | |9.12. |
| − | | | + | |Evolutionary robotics |
| − | |R&N, ch.25 | + | |R&N, ch. 25.1.-25.2, 25.7 |
| + | |- | ||
| + | |16.12. | ||
| + | |Quo vadis AI? Problems and visions of future AI methods | ||
| + | |R&N, ch. 26 | ||
|} | |} | ||
== References == | == References == | ||
| − | * 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. ''R&N''.<!-- 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 == | ||
| − | * Exercises ( | + | The course grading consists of three parts: |
| − | * | + | * Exercises (36%) |
| − | * | + | * 5-min exams (10%) |
| − | * | + | * Project (14%) |
| + | * Final exam (40%) | ||
| + | Throughout the semester, you can gain 36% for exercises, 10% for 5-min exams, 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 40% of the total mark. | ||
| + | |||
| + | '''Overall grading:''' A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0). | ||
Aktuálna revízia z 19:51, 16. september 2024
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 | A | Ľubica Beňušková |
| Exercises (DAV) | Wednesday | 18:10 | I-H3 | Štefan Pócoš, Iveta Bečková, Branislav Zigo |
| Exercises (AIN) | Friday | 9:50 | I-H6 | Štefan Pócoš, Iveta Bečková, Branislav Zigo |
Lecture syllabus
| Date | Topic | References |
|---|---|---|
| 23.9. | Introduction, basic notions for intelligent agents, reflex agents, reflex agents with a state | R&N, ch. 2 |
| 30.9. | Search: uninformed, informed, uniform cost, DBS, BFS, A*, Min Max, greedy, alpha-beta, adversarial | R&N, ch. 3.2.2 -3.5.2, ch. 5.1-5.3 |
| 7.10. | Constraint Satisfaction Problems (CSP), heuristics, methods of solving | R&N, ch. 6.1 - 6.3.2 |
| 14.10. | Logical agents: inference in logical knowledge base | R&N, ch. 7.1 - 7.5, 9.3 - 9.4 |
| 21.10. | Local and global optimization, hill climbing, GA, simulated annealing | R&N, ch. 4.1 - 4.2 |
| 28.10. | Introduction to probability, reasoning with uncertainty, Bayes' rule | R&N, ch. 13.1 -13.5 |
| 4.11. | Introduction to nature-inspired computing, inductive learning, regression | R&N, ch.18.1-18.2, 18.6.1-18.6.2 |
| 11.11. | Single perceptron, delta rule for supervised learning | R&N, ch.18.6.3-18.6.4 |
| 18.11. | Multi-layer perceptron, supervised learning by error backpropagation | R&N, ch.18.7 |
| 25.11. | Unsupervised learning: K-means clustering, KNN, Self-organizing map | R&N, ch.18.8-18.8.2 |
| 2.12. | Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet | Artificial Neural Networks |
| 9.12. | Evolutionary robotics | R&N, ch. 25.1.-25.2, 25.7 |
| 16.12. | 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. R&N.
- 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%)
- 5-min exams (10%)
- Project (14%)
- Final exam (40%)
Throughout the semester, you can gain 36% for exercises, 10% for 5-min exams, 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 40% of the total mark.
Overall grading: A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0).