(5 intermediate revisions by the same user not shown)
Line 65: Line 65:
 
|Logical agents:  inference in logical knowledge base.
 
|Logical agents:  inference in logical knowledge base.
 
|R&N, ch.7
 
|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.
 
|9.11.
|Logical agents:  inference in logical knowledge base.
+
|Single perceptron, delta rule.
|R&N, ch.7
+
|R&N, ch.18.6.3-18.6.4
 
|-
 
|-
 
|16.11.
 
|16.11.
|Supervised learning: linear and non-linear regression, binary perceptron.
+
|Rektorské voľno od 12:00 h.
|R&N, ch.18.1-18.2, 18.6-18.6.3
+
|
 
|-
 
|-
 
|23.11.
 
|23.11.
|Multi-layer perceptron, idea of error backpropagation.
+
|Multi-layer perceptron, error backpropagation.
|R&N, ch.18.6.4-18.7.5
+
|R&N, ch.18.7
 
|-
 
|-
|30.12.
+
|30.11.
|Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet.
+
|Unsupervised learning: K-means clustering, KNN, Self-organizing map.
|[http://cogsci.fmph.uniba.sk/~holas/res/UI-ch6-ANN.pdf  Artificial Neural Networks]
+
|R&N, ch.18.8-18.8.2
 
|-
 
|-
 
|7.12.
 
|7.12.
|Unsupervised learning: K-means clustering, KNN, Self-organizing map, Principal component analysis.
+
| Applications of multi-layer perceptron: sonar, NetTalk, ALVINN, LeNet
|R&N, ch.18.8-18.8.2
+
|[http://cogsci.fmph.uniba.sk/~holas/res/UI-ch6-ANN.pdf  Artificial Neural Networks]
 
|-
 
|-
 
|14.12.
 
|14.12.

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).