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Riadok 8: Riadok 8:
 
<!--[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://list.fmph.uniba.sk LIST].
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For exercises, project assignments, and lecture slides, please see the course in [https://moodle.uniba.sk/course/view.php?id=3062 moodle].
 +
 
  
== News ==
 
''No news yet''
 
  
 
== Course schedule ==
 
== Course schedule ==
Riadok 22: Riadok 21:
 
|-
 
|-
 
|Lecture
 
|Lecture
|Thursday
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|Tuesday
|11:30
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|14:00
|M-X
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|F1-108
 
|[[Maria Markosova|Mária Markošová]],  [[Lubica Benuskova|Ľubica Beňušková]]
 
|[[Maria Markosova|Mária Markošová]],  [[Lubica Benuskova|Ľubica Beňušková]]
 
|-
 
|-
 
|Exercises
 
|Exercises
|Wednesday
+
|Monday
|9:50
+
|10:40
 
|I-H3
 
|I-H3
|[[Juraj Holas|Juraj Holas]]
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|[[Stefan Pocos|Štefan Pócoš]], [[Iveta Beckova|Iveta Bečková]]
  
 
|}
 
|}
Riadok 43: Riadok 42:
 
!References
 
!References
 
|-
 
|-
|27.09.
+
|21.9.
 
|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
 
|-
 
|-
|04.10.
+
|28.9.
 
|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
 
|-
 
|-
|11.10.
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|5.10.
|Constraint satisfaction problem: definition, heuristics, methods of solving.
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|R&N, ch.6
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|-
+
|18.10.
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|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
 
|-
 
|-
|25.10.
+
|12.10.
 +
|Constraint satisfaction problem: definition, heuristics, methods of solving.
 +
|R&N, ch.6
 +
|-
 +
|19.10.
 
|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
 
|-
 
|-
|08.11.
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|26.10.
 
|Logical agents:  inference in logical knowledge base.
 
|Logical agents:  inference in logical knowledge base.
 
|R&N, ch.7
 
|R&N, ch.7
 
|-
 
|-
|15.11.
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|9.11.
 +
|Logical agents:  inference in logical knowledge base.
 +
|R&N, ch.7
 +
|-
 +
|16.11.
 
|Supervised learning: linear and non-linear regression, binary perceptron.
 
|Supervised learning: linear and non-linear regression, binary perceptron.
 
|R&N, ch.18.1-18.2, 18.6-18.6.3
 
|R&N, ch.18.1-18.2, 18.6-18.6.3
 
|-
 
|-
|22.11.
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|23.11.
 
|Multi-layer perceptron, idea of error backpropagation.
 
|Multi-layer perceptron, idea of error backpropagation.
 
|R&N, ch.18.6.4-18.7.5
 
|R&N, ch.18.6.4-18.7.5
 
|-
 
|-
|29.11.
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|30.12.
|Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet
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|Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet.
|
+
|[http://cogsci.fmph.uniba.sk/~holas/res/UI-ch6-ANN.pdf  Artificial Neural Networks]
 
|-
 
|-
|06.12.
+
|7.12.
|Unsupervised learning: K-means clustering, KNN, Self-organizing map, Principal component analysis
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|Unsupervised learning: K-means clustering, KNN, Self-organizing map, Principal component analysis.
 
|R&N, ch.18.8-18.8.2
 
|R&N, ch.18.8-18.8.2
 
|-
 
|-
|13.12.
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|14.12.
|Weight optimization of MLP using genetic algorithms, evolutionary robotics
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|Quo vadis AI? Problems and visions of future AI methods.
|
+
|-
+
|20.12.
+
|Quo vadis AI? Problems and visions of future AI methods
+
 
|R&N, ch. 26
 
|R&N, ch. 26
 
|}
 
|}
Riadok 95: Riadok 94:
  
  
* Russell S., Norwig P. (2010). [http://cogsci.fmph.uniba.sk/~holas/res/AIMA3.pdf Artificial Intelligence: A Modern Approach], (3rd ed.), Prentice Hall. Ask lecturers for password. Also available in the faculty library. A.k.a. ''AIMA''.<!-- user:uui, password:uui -->
+
* 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 -->
* Marsland S. (2015). [http://cogsci.fmph.uniba.sk/~holas/res/ML_Marsland.pdf Machine Learning: An Algorithmic Perspective], (2nd ed.), CRC Press. Ask lecturers for password.<!-- user:uui, password:uui -->
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* Návrat P., Bieliková M., Beňušková Ľ. et al., [https://knihy.heureka.sk/umela-inteligencia-pavol-navrat Umelá inteligencia (3. vydanie)], Vydavateľstvo STU, 2015. ([http://cogsci.fmph.uniba.sk/~holas/res/UI-ch6-ANN.pdf ANN chapter])
* Zadeh L. (2007). [http://dx.doi.org/10.4249/scholarpedia.1766 Fuzzy logic], Scholarpedia, 3(3):1766.
+
  
 
== Course grading ==
 
== Course grading ==
  
 
The course grading consists of four parts:
 
The course grading consists of four parts:
* Exercises (25%)
+
* Exercises (30%)
* Short tests (10%)
+
* Project (20%)
* Project (15%)
+
 
* Final exam (50%)
 
* 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.)
+
Throughout the semester, you can gain 30% for exercises and 20% 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 12:20, 17. september 2021

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 the course in moodle.


Course schedule

Type Day Time Room Lecturer
Lecture Tuesday 14:00 F1-108 Mária Markošová, Ľubica Beňušková
Exercises Monday 10:40 I-H3 Štefan Pócoš, Iveta Bečková


Lecture syllabus

Date Topic References
21.9. What is artificial intelligence, properties and types of agents. Uninformed search - state space, uninformed search algorithms, DFS, BFS. R&N, ch.2-3.4
28.9. Informed search, A* algorithm, heuristics and their properties. R&N, ch.3.5-3.6
5.10. Local search, looking for an optimum, hill climbing, genetic algorithm, simulated annealing etc. R&N, ch.4.1
12.10. Constraint satisfaction problem: definition, heuristics, methods of solving. R&N, ch.6
19.10. Basics of game theory, MiniMax algorithm, Alpha-Beta pruning, ExpectiMiniMax. R&N, ch.5
26.10. Logical agents: inference in logical knowledge base. R&N, ch.7
9.11. Logical agents: inference in logical knowledge base. R&N, ch.7
16.11. Supervised learning: linear and non-linear regression, binary perceptron. R&N, ch.18.1-18.2, 18.6-18.6.3
23.11. Multi-layer perceptron, idea of error backpropagation. R&N, ch.18.6.4-18.7.5
30.12. Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet. Artificial Neural Networks
7.12. Unsupervised learning: K-means clustering, KNN, Self-organizing map, Principal component analysis. R&N, ch.18.8-18.8.2
14.12. Quo vadis AI? Problems and visions of future AI methods. R&N, ch. 26

References

Course grading

The course grading consists of four parts:

  • Exercises (30%)
  • Project (20%)
  • Final exam (50%)

Throughout the semester, you can gain 30% for exercises and 20% 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).