m (Removal of 25% limit for exam.)
 
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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://list.fmph.uniba.sk LIST].
+
For exercises, project assignments, and lecture slides, please see the course in [https://moodle.uniba.sk/course/view.php?id=3062 moodle].
 +
 
  
== News ==
 
* Cvičenia 26.9. odpadávajú kvôli rektorskému voľnu.
 
  
 
== Course schedule ==
 
== Course schedule ==
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|-
 
|-
 
|Lecture
 
|Lecture
|Thursday
+
|Tuesday
|11:30
+
|14:00
|M-X
+
|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]]
+
|[[Stefan Pocos|Štefan Pócoš]], [[Iveta Beckova|Iveta Bečková]]
  
 
|}
 
|}
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!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.
+
|5.10.
|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.
 
|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.
+
|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.
+
|2.11.
|Supervised learning: linear and non-linear regression, binary perceptron.
+
|Introduction to nature-inspired computing, inductive learning, regression.
|R&N, ch.18.1-18.2, 18.6-18.6.3
+
|R&N, ch.18.1-18.2, 18.6.1-18.6.2
 
|-
 
|-
|22.11.
+
|9.11.
|Multi-layer perceptron, idea of error backpropagation.
+
|Single perceptron, delta rule.
|R&N, ch.18.6.4-18.7.5
+
|R&N, ch.18.6.3-18.6.4
 
|-
 
|-
|29.11.
+
|16.11.
|Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet
+
|Rektorské voľno od 12:00 h.
 
|
 
|
 
|-
 
|-
|06.12.
+
|23.11.
|Unsupervised learning: K-means clustering, KNN, Self-organizing map, Principal component analysis
+
|Multi-layer perceptron, error backpropagation.
 +
|R&N, ch.18.7
 +
|-
 +
|30.11.
 +
|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
 
|-
 
|-
|13.12.
+
|7.12.
|Weight optimization of MLP using genetic algorithms, evolutionary robotics
+
| Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet
|
+
|[http://cogsci.fmph.uniba.sk/~holas/res/UI-ch6-ANN.pdf  Artificial Neural Networks]
 
|-
 
|-
|20.12.
+
|14.12.
|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
 
|}
 
|}
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== Course grading ==
 
== Course grading ==
  
The course grading consists of four parts:
+
The course grading consists of three parts:
* Exercises (25%)
+
* Exercises (30%)
* Short tests (10%)
+
* Project (20%)
* Project (15%)
+
 
* Final exam (50%)
 
* Final exam (50%)
You can gain 25% for exercises, 10% for tests and 15% for the project. You have to earn at least half from each of these. The final exam is worth 50% of the total mark. If you do not meet minimal condition from the semester, then you cannot pass the exam.
+
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).

Latest revision as of 09:09, 29 November 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
2.11. Introduction to nature-inspired computing, inductive learning, regression. R&N, ch.18.1-18.2, 18.6.1-18.6.2
9.11. Single perceptron, delta rule. R&N, ch.18.6.3-18.6.4
16.11. Rektorské voľno od 12:00 h.
23.11. Multi-layer perceptron, error backpropagation. R&N, ch.18.7
30.11. Unsupervised learning: K-means clustering, KNN, Self-organizing map. R&N, ch.18.8-18.8.2
7.12. Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet Artificial Neural Networks
14.12. Quo vadis AI? Problems and visions of future AI methods. R&N, ch. 26

References

Course grading

The course grading consists of three 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).