(Uprava sylabu a informacii pre ZS2018)
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, please see [http://list.fmph.uniba.sk LIST].
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For exercises, project assignments, and lecture slides, please see [https://list.fmph.uniba.sk LIST].
  
 
== News ==
 
== News ==
'''Exam (test):''' Thursday, 18. 1. 2018 at 9:00. Please sign up.
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''No news yet''
 
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<!-- [[#Archív noviniek|Archív noviniek…]] -->
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== Course schedule ==
 
== Course schedule ==
Riadok 26: Riadok 24:
 
|Thursday
 
|Thursday
 
|11:30
 
|11:30
|M-IV
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|M-X
 
|[[Maria Markosova|Mária Markošová]],  [[Lubica Benuskova|Ľubica Beňušková]]
 
|[[Maria Markosova|Mária Markošová]],  [[Lubica Benuskova|Ľubica Beňušková]]
 
|-
 
|-
|Exercises (students of m/AIN, m/INF)
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|Exercises
|Monday (next week)
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|Wednesday
 
|9:50
 
|9:50
|H-6
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|I-H3
 
|[[Juraj Holas|Juraj Holas]]
 
|[[Juraj Holas|Juraj Holas]]
  
Riadok 38: Riadok 36:
  
  
== Syllabus ==
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== Lecture syllabus ==
  
 
{| class="alternative table-responsive"
 
{| class="alternative table-responsive"
Riadok 45: Riadok 43:
 
!References
 
!References
 
|-
 
|-
|28.09.
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|27.09.
|What is artificial intelligence, agent-robot going around an obstacle, properties and types of agents.
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|What is artificial intelligence, properties and types of agents. Uninformed search - state space, uninformed search algorithms, DFS, BFS.
|R&N, ch.2
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|R&N, ch.2-3.4
 
|-
 
|-
|05.10.
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|04.10.
|Search, state space, tree search, searching agent, uninformed search, informed search, graph search versus tree search, heuristics and their properties.
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|Informed search, A* algorithm, heuristics and their properties.
|R&N,ch.3.1-4
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|R&N, ch.3.5-3.6
 
|-
 
|-
|12.10.
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|11.10.
|Local search search, looking for an optimum, hill climbing, genetic algorithm, simulated annealing etc.
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|R&N, ch.3.5-6
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|-
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|19.10.
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|Constraint satisfaction problem: definition, heuristics, methods of solving.  
 
|Constraint satisfaction problem: definition, heuristics, methods of solving.  
 
|R&N, ch.6
 
|R&N, ch.6
 
|-
 
|-
|26.10.
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|18.10.
|Basics of game theory, minimax algorithm, alpha beta pruning, expectiminimax.
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|Local search, looking for an optimum, hill climbing, genetic algorithm, simulated annealing etc.
 +
|R&N, ch.4.1
 +
|-
 +
|25.10.
 +
|Basics of game theory, MiniMax algorithm, Alpha-Beta pruning, ExpectiMiniMax.
 
|R&N, ch.5
 
|R&N, ch.5
 
|-
 
|-
|02.11.
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|08.11.
|More complex logical agents:  inference in logical knowledge base.
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|Logical agents:  inference in logical knowledge base.
 
|R&N, ch.7
 
|R&N, ch.7
 
|-
 
|-
|09.11.
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|15.11.
|Learning from examples: supervised learning, perceptron, classification, regression, model selection, generalization.
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|Supervised learning: linear and non-linear regression, binary perceptron.
|R&N, ch.18.1-7
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|R&N, ch.18.1-18.2, 18.6-18.6.3
 
|-
 
|-
|16.11.
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|22.11.
|Unsupervised learning in neural networks: Principal component analysis, Self-organizing map.
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|Multi-layer perceptron, idea of error backpropagation.
|Marsland (2015), ch.6 & 14
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|R&N, ch.18.6.4-18.7.5
 
|-
 
|-
|23.11.
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|29.11.
|Probabilistic learning: basic concepts and methods (Bayes formula).
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|Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet
|R&N, ch.13,20
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|
 
|-
 
|-
|30.11.
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|06.12.
|Reinforcement learning, basic concepts, methods of learning.
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|Unsupervised learning: K-means clustering, KNN, Self-organizing map, Principal component analysis
|R&N, ch.21
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|R&N, ch.18.8-18.8.2
 
|-
 
|-
|07.12.
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|13.12.
|Fuzzy systems, fuzzy logic and reasoning.
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|Weight optimization of MLP using genetic algorithms, evolutionary robotics
|Zadeh (2007)
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|
 
|-
 
|-
|14.12.
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|20.12.
|Robotics: basic concepts and tasks.
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|Quo vadis AI? Problems and visions of future AI methods
|R&N, ch.25
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|R&N, ch. 26
 
|}
 
|}
  
 
== References ==
 
== References ==
  
* Russell S., Norwig P. (2010). [http://dai.fmph.uniba.sk/courses/ICI/References/russell-norvig.AI-modern-approach.3ed.2010.pdf Artificial Intelligence: A Modern Approach], (3rd ed.), Prentice Hall. Available in the faculty library.
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* Russell S., Norwig P. (2010). [http://dai.fmph.uniba.sk/courses/ICI/References/russell-norvig.AI-modern-approach.3ed.2010.pdf Artificial Intelligence: A Modern Approach], (3rd ed.), Prentice Hall. Available in the faculty library. A.k.a. ''AIMA''.
 
* Marsland S. (2015). [http://dai.fmph.uniba.sk/courses/ICI/References/marsland.machine-learning.2ed.2015.pdf Machine Learning: An Algorithmic Perspective], (2nd ed.), CRC Press.
 
* Marsland S. (2015). [http://dai.fmph.uniba.sk/courses/ICI/References/marsland.machine-learning.2ed.2015.pdf Machine Learning: An Algorithmic Perspective], (2nd ed.), CRC Press.
 
* Zadeh L. (2007). [http://dx.doi.org/10.4249/scholarpedia.1766 Fuzzy logic], Scholarpedia, 3(3):1766.  
 
* Zadeh L. (2007). [http://dx.doi.org/10.4249/scholarpedia.1766 Fuzzy logic], Scholarpedia, 3(3):1766.  
Riadok 102: Riadok 100:
 
== Course grading ==
 
== Course grading ==
  
* Exercises (25%).
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The course grading consists of four parts:
* Final exam (50%).
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* Exercises (25%)
* Projects (25%)
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* Short tests (10%)
* <b>Overall grading:</b> A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0).
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* Project (15%)
 +
* 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.)
 +
 
 +
'''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 14:27, 18. september 2018

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 LIST.

News

No news yet

Course schedule

Type Day Time Room Lecturer
Lecture Thursday 11:30 M-X Mária Markošová, Ľubica Beňušková
Exercises Wednesday 9:50 I-H3 Juraj Holas


Lecture syllabus

Date Topic References
27.09. What is artificial intelligence, properties and types of agents. Uninformed search - state space, uninformed search algorithms, DFS, BFS. R&N, ch.2-3.4
04.10. Informed search, A* algorithm, heuristics and their properties. R&N, ch.3.5-3.6
11.10. 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. R&N, ch.4.1
25.10. Basics of game theory, MiniMax algorithm, Alpha-Beta pruning, ExpectiMiniMax. R&N, ch.5
08.11. Logical agents: inference in logical knowledge base. R&N, ch.7
15.11. Supervised learning: linear and non-linear regression, binary perceptron. R&N, ch.18.1-18.2, 18.6-18.6.3
22.11. Multi-layer perceptron, idea of error backpropagation. R&N, ch.18.6.4-18.7.5
29.11. Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet
06.12. Unsupervised learning: K-means clustering, KNN, Self-organizing map, Principal component analysis R&N, ch.18.8-18.8.2
13.12. Weight optimization of MLP using genetic algorithms, evolutionary robotics
20.12. 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 (25%)
  • Short tests (10%)
  • Project (15%)
  • 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.)

Overall grading: A (100-91), B (90-81), C (80-71), D (70-61), E (60-51), Fx (50-0).