(→Syllabus) |
(→Syllabus) |
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| Riadok 44: | Riadok 44: | ||
|- | |- | ||
|1. | |1. | ||
| − | | | + | |18.09. |
|What is computational intelligence, basic concepts, relation to artificial intelligence. | |What is computational intelligence, basic concepts, relation to artificial intelligence. | ||
<!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-def.4x.pdf slides]--> | <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-def.4x.pdf slides]--> | ||
| Riadok 50: | Riadok 50: | ||
|- | |- | ||
|2. | |2. | ||
| − | | | + | |25.09. |
|Taxonomy of artificial agents, nature of environments. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-agents.4x.pdf slides]--> | |Taxonomy of artificial agents, nature of environments. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-agents.4x.pdf slides]--> | ||
|R&N (2010), chap.2 | |R&N (2010), chap.2 | ||
|- | |- | ||
|3. | |3. | ||
| − | | | + | |02.10. |
|Inductive learning via observations, decision trees. Model selection. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-learning.4x.pdf slides]--> | |Inductive learning via observations, decision trees. Model selection. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-learning.4x.pdf slides]--> | ||
|R&N (2010), ch.18.1-3,18.6; Marsland (2015), ch.12 <!--[http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ DT visualization], [https://www.youtube.com/watch?v=LDRbO9a6XPU DT in python], [https://www.youtube.com/watch?v=2s3aJfRr9gE information entropy], [https://www.youtube.com/watch?v=EuBBz3bI-aA bias-variance tradeoff]--> | |R&N (2010), ch.18.1-3,18.6; Marsland (2015), ch.12 <!--[http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ DT visualization], [https://www.youtube.com/watch?v=LDRbO9a6XPU DT in python], [https://www.youtube.com/watch?v=2s3aJfRr9gE information entropy], [https://www.youtube.com/watch?v=EuBBz3bI-aA bias-variance tradeoff]--> | ||
|- | |- | ||
|4. | |4. | ||
| − | | | + | |09.10. |
|Supervised learning in feedforward neural networks (perceptrons), pattern classification, regression. | |Supervised learning in feedforward neural networks (perceptrons), pattern classification, regression. | ||
<!-- [http://dai.fmph.uniba.sk/courses/ICI/References/ci-fwdnn.4x.pdf slides] --> | <!-- [http://dai.fmph.uniba.sk/courses/ICI/References/ci-fwdnn.4x.pdf slides] --> | ||
| Riadok 66: | Riadok 66: | ||
|- | |- | ||
|5. | |5. | ||
| − | | | + | |16.10. |
|Unsupervised (self-organizing) neural networks: feature extraction, data visualization. | |Unsupervised (self-organizing) neural networks: feature extraction, data visualization. | ||
<!-- [http://dai.fmph.uniba.sk/courses/ICI/References/ci-unsup.4x.pdf slides]--> | <!-- [http://dai.fmph.uniba.sk/courses/ICI/References/ci-unsup.4x.pdf slides]--> | ||
| Riadok 72: | Riadok 72: | ||
|- | |- | ||
|6. | |6. | ||
| − | | | + | |23.10. |
|Probability theory. Bayes formula. Naive Bayes classifier. | |Probability theory. Bayes formula. Naive Bayes classifier. | ||
<!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-prob.4x.pdf slides]--> | <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-prob.4x.pdf slides]--> | ||
| Riadok 78: | Riadok 78: | ||
|- | |- | ||
|7. | |7. | ||
| − | | | + | |30.10. |
|Probabilistic learning: MAP, ML | |Probabilistic learning: MAP, ML | ||
|R&N (2010), ch.13,20.1-2 | |R&N (2010), ch.13,20.1-2 | ||
|- | |- | ||
|8. | |8. | ||
| − | | | + | |06.11. |
|Q&As before mid-term | |Q&As before mid-term | ||
|Tuesday: mid-term test | |Tuesday: mid-term test | ||
|- | |- | ||
|9. | |9. | ||
| − | | | + | |13.11. |
|Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-rl.4x.pdf slides] | |Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-rl.4x.pdf slides] | ||
|R&N (2010), ch.21.1-2. | |R&N (2010), ch.21.1-2. | ||
|- | |- | ||
|10. | |10. | ||
| − | | | + | |20.11. |
|Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains. | |Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains. | ||
|R&N (2010), ch.21.3-5. | |R&N (2010), ch.21.3-5. | ||
|- | |- | ||
|11. | |11. | ||
| − | | | + | |27.11. |
|Evolutionary computation: basic concepts, genetic algorithms. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-evol.4x.pdf slides] | |Evolutionary computation: basic concepts, genetic algorithms. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-evol.4x.pdf slides] | ||
|Engelbrecht (2007), ch.8 | |Engelbrecht (2007), ch.8 | ||
|- | |- | ||
|12. | |12. | ||
| − | | | + | |04.12. |
|Fuzzy systems, fuzzy logic and reasoning. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-fuzzy.4x.pdf slides] | |Fuzzy systems, fuzzy logic and reasoning. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-fuzzy.4x.pdf slides] | ||
|Engelbrecht (2007), ch.20-21; Zadeh (2007) | |Engelbrecht (2007), ch.20-21; Zadeh (2007) | ||
<!-- | <!-- | ||
|13. | |13. | ||
| − | | | + | |11.12. |
|Explainable artificial intelligence (XAI) + Revision of main concepts. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-xai.4x.pdf slides] | |Explainable artificial intelligence (XAI) + Revision of main concepts. <!--[http://dai.fmph.uniba.sk/courses/ICI/References/ci-xai.4x.pdf slides] | ||
|Barreto Arrieta A. et al. (2020) | |Barreto Arrieta A. et al. (2020) | ||
Verzia zo dňa a času 15:06, 28. august 2023
Introduction to Computational Intelligence 2-IKVa-115/18
Obsah
The course objectives are to make the students familiar with basic principles of various computational methods of data processing that can commonly be called computational intelligence (CI). This includes mainly bottom-up approaches to solutions of (hard) problems based on various heuristics (soft computing), rather than exact approaches of traditional artificial intelligence based on logic (hard computing). Examples of CI are nature-inspired methods (artificial neural networks, evolutionary algorithms, fuzzy systems), as well as probabilistic methods and reinforcement learning. After the course the students will be able to conceptually understand the important terms and algorithms of CI, and choose appropriate method(s) for a given task. The theoretical lectures are combined with the seminar where the important concepts will be discussed and practical examples will be shown.
Course schedule
| Type | Day | Time | Room | Lecturer |
|---|---|---|---|---|
| Lecture | Monday | 9:00 - 10:30 | I-9 / in room | Igor Farkaš |
| Seminar | TBA | TBA | i-23 / in room | Kristína Malinovská |
Syllabus
| week | Date | Topic | References |
|---|---|---|---|
| 1. | 18.09. | What is computational intelligence, basic concepts, relation to artificial intelligence. | Craenen & Eiben (2003); wikipedia; R&N (2010), chap.1; |
| 2. | 25.09. | Taxonomy of artificial agents, nature of environments. | R&N (2010), chap.2 |
| 3. | 02.10. | Inductive learning via observations, decision trees. Model selection. | R&N (2010), ch.18.1-3,18.6; Marsland (2015), ch.12 |
| 4. | 09.10. | Supervised learning in feedforward neural networks (perceptrons), pattern classification, regression. | R&N (2010), ch.18.2; Marsland (2015), ch.3-4, Engelbrecht (2007), ch.2-3 |
| 5. | 16.10. | Unsupervised (self-organizing) neural networks: feature extraction, data visualization. | Marsland (2015), ch.14, Engelbrecht (2007), ch.4 |
| 6. | 23.10. | Probability theory. Bayes formula. Naive Bayes classifier. | R&N (2010), ch.13,20.1-2 |
| 7. | 30.10. | Probabilistic learning: MAP, ML | R&N (2010), ch.13,20.1-2 |
| 8. | 06.11. | Q&As before mid-term | Tuesday: mid-term test |
| 9. | 13.11. | Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. slides | R&N (2010), ch.21.1-2. |
| 10. | 20.11. | Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains. | R&N (2010), ch.21.3-5. |
| 11. | 27.11. | Evolutionary computation: basic concepts, genetic algorithms. slides | Engelbrecht (2007), ch.8 |
| 12. | 04.12. | Fuzzy systems, fuzzy logic and reasoning. slides | Engelbrecht (2007), ch.20-21; Zadeh (2007) |
Note: Dates refer to lectures, seminars will be on day+3 each week.
References
- Craenen B., Eiben A. (2003): Computational Intelligence. In: Encyclopedia of Life Support Sciences, EOLSS Publishers Co.
- Engelbrecht A. (2007). Computational Intelligence: An Introduction (2nd ed.), John Willey & Sons.
- Russell S., Norwig P. (2010). Artificial Intelligence: A Modern Approach, (3rd ed.), Prentice Hall. Available in the faculty library.
- Marsland S. (2015). Machine Learning: An Algorithmic Perspective, (2nd ed.), CRC Press.
- Zadeh L. (2007). Fuzzy logic, Scholarpedia, 3(3):1766.
Course grading
- Active participation during the lectures/exercises (25%): 15 for lectures, 10 for exercises. Minimum 1/3 of points required.
- Homework (10%): weekly homework given and discussed at the exercises, usually solved by hand or via excel sheets (no programming necessary)
- Written mid-term test (30%), covering topics of the first half of the semester.
- Final written-oral exam (30%): We will discuss 3 randomly chosen (by a computer) questions that basically correspond to weekly topics during the semester. Minimum of 1/3 of all points required.
- Small final project (10%) = implementation of a small neural network (using an existing Python library) and writing a short report. Note: even without this, the student can still get maximum points if s/he has performed very actively. Deadline: TBA
- Overall grading: A (>90%), B (>80%), C (>70%), D (>60%), E (>50%), Fx (otherwise).