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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, please see moodle.
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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].
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== News ==
 
<!-- [[#Archív noviniek|Archív noviniek…]] -->
 
  
 
== Course schedule ==
 
== Course schedule ==
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|-
 
|-
 
|Lecture
 
|Lecture
|Thursday
+
|Tuesday
|11:30
+
|14:00
|M-IV
+
|F1-108
|[[Maria Markosova|Mária Markošová]],  [[Igor Farkas|Igor Farkaš]]
+
|[[Maria Markosova|Mária Markošová]],  [[Lubica Benuskova|Ľubica Beňušková]]
 
|-
 
|-
|Exercises (students of mIKV)
+
|Exercises
|Thursday
+
|Monday
|16:30
+
|10:40
|H-6
+
|I-H3
|[[Peter Gergel|Peter Gergeľ]]
+
|[[Stefan Pocos|Štefan Pócoš]], [[Iveta Beckova|Iveta Bečková]]
|-
+
|Exercises (other students)
+
|Monday (next week)
+
|9:50
+
|H-6
+
|[[Juraj Holas|Juraj Holas]]
+
  
 
|}
 
|}
  
  
== Syllabus ==
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== Lecture syllabus ==
  
 
{| class="alternative table-responsive"
 
{| class="alternative table-responsive"
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!References
 
!References
 
|-
 
|-
|28.09.
+
|21.9.
|What is artificial intelligence, agent-robot going around an obstacle, properties and types of agents.
+
|What is artificial intelligence, properties and types of agents. Uninformed search - state space, uninformed search algorithms, DFS, BFS.
|R&N, chap.X
+
|R&N, ch.2-3.4
 
|-
 
|-
|05.10.
+
|28.9.
|Search, state space, tree search, searching agent, uninformed search.
+
|Informed search, A* algorithm, heuristics and their properties.
|R&N,ch.X
+
|R&N, ch.3.5-3.6
 
|-
 
|-
|12.10.
+
|5.10.
|Informed search, graph search versus tree search, methods of implementation, heuristics and their properties.
+
|Local search, looking for an optimum, hill climbing, genetic algorithm, simulated annealing etc.
|R&N, ch.X
+
|R&N, ch.4.1
 
|-
 
|-
|19.10.
+
|12.10.
 
|Constraint satisfaction problem: definition, heuristics, methods of solving.  
 
|Constraint satisfaction problem: definition, heuristics, methods of solving.  
 
|R&N, ch.6
 
|R&N, ch.6
 +
|-
 +
|19.10.
 +
|Basics of game theory, MiniMax algorithm, Alpha-Beta pruning, ExpectiMiniMax.
 +
|R&N, ch.5
 
|-
 
|-
 
|26.10.
 
|26.10.
|Basics of game theory, minimax algorithm.
+
|Logical agents:  inference in logical knowledge base.
|R&N, ch.X
+
|R&N, ch.7
 
|-
 
|-
|02.11.
+
|2.11.
|More complex agents: making inferences and learning. Propositional logic, making inferences.
+
|Introduction to nature-inspired computing, inductive learning, regression.
|R&N, ch.X
+
|R&N, ch.18.1-18.2, 18.6.1-18.6.2
 
|-
 
|-
|09.11.
+
|9.11.
|Learning from examples: supervised learning, perceptron, classification, regression, model selection, generalization, regularization.
+
|Single perceptron, delta rule.
|R&N, ch.18.x
+
|R&N, ch.18.6.3-18.6.4
 
|-
 
|-
 
|16.11.
 
|16.11.
|Nonparametric models, methods of nearest neighobors, finding nearest neighbors with k-d trees, regression.
+
|Rektorské voľno od 12:00 h.
|R&N, ch.18.y
+
|
 
|-
 
|-
 
|23.11.
 
|23.11.
|Probabilistic computation: basic concepts and methods (Bayes formula).
+
|Multi-layer perceptron, error backpropagation.
|R&N, ch.13,20
+
|R&N, ch.18.7
 
|-
 
|-
 
|30.11.
 
|30.11.
|Reinforcement learning, basic concepts, methods of learning.
+
|Unsupervised learning: K-means clustering, KNN, Self-organizing map.
|R&N, ch.21
+
|R&N, ch.18.8-18.8.2
 
|-
 
|-
|07.12.
+
|7.12.
|Fuzzy systems, fuzzy logic and reasoning.
+
| Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet
|Zadeh (2007)
+
|[http://cogsci.fmph.uniba.sk/~holas/res/UI-ch6-ANN.pdf  Artificial Neural Networks]
 
|-
 
|-
 
|14.12.
 
|14.12.
|Robotics: basic concepts and tasks.
+
|Quo vadis AI? Problems and visions of future AI methods.
|R&N, ch.25
+
|R&N, ch. 26
 
|}
 
|}
  
 
== References ==
 
== References ==
  
* Russell S., Norwig P. (2010). [http://dai.fmph.uniba.sk/courses/ICI/russell-norvig.AI-modern-approach.3rd-ed.2010.pdf Artificial Intelligence: A Modern Approach], (3rd ed.). Available in the faculty library.
+
 
* Zadeh L. (2007). [http://dx.doi.org/10.4249/scholarpedia.1766 Fuzzy logic], Scholarpedia, 3(3):1766.  
+
* 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://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])
  
 
== Course grading ==
 
== Course grading ==
  
* Exercises (30%).
+
The course grading consists of three parts:
* Final exam (50%).
+
* Exercises (30%)
* Projects (20%)
+
* Project (20%)
* <b>Overall grading:</b> A (50-46), B (45-41), C (40-36), D (35-31), E (30-26), Fx (25-0).
+
* 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).

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).