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== News == | == News == | ||
+ | '''Exam:''' The list of questions is [http://dai.fmph.uniba.sk/courses/ICI/ci-exam-questions.pdf here]. You will choose three questions (pseudo)randomly. | ||
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− | + | ;16. 9. 2018 | |
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− | ; | + | |
: We start on Monday, 24th September. | : We start on Monday, 24th September. | ||
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Riadok 34: | Riadok 33: | ||
|- | |- | ||
|Seminar | |Seminar | ||
− | | | + | |Thursday |
|14:00 - 15:30 | |14:00 - 15:30 | ||
|I-9 | |I-9 | ||
− | |[[Igor Farkas|Igor Farkaš]] & [ | + | |[[Igor Farkas|Igor Farkaš]] & [[Endre Hamerlik]] |
|} | |} | ||
Riadok 48: | Riadok 47: | ||
!References | !References | ||
|- | |- | ||
− | | | + | |23.09. |
|What is computational intelligence, basic concepts, relation to artificial intelligence. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-def.4x.pdf slides] | |What is computational intelligence, basic concepts, relation to artificial intelligence. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-def.4x.pdf slides] | ||
|Craenen & Eiben (2003); [https://en.wikipedia.org/wiki/Computational_intelligence wikipedia]; R&N (2010), chap.1; Sloman (2002) | |Craenen & Eiben (2003); [https://en.wikipedia.org/wiki/Computational_intelligence wikipedia]; R&N (2010), chap.1; Sloman (2002) | ||
|- | |- | ||
− | | | + | |30.09. |
|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 | ||
|- | |- | ||
− | | | + | |07.10. |
|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 (2009), ch.6.1-2, [http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ visualization], [http://fiddle.jshell.net/92Jxj/show/light/ interactive demo] | |R&N (2010), ch.18.1-3,18.6; Marsland (2009), ch.6.1-2, [http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ visualization], [http://fiddle.jshell.net/92Jxj/show/light/ interactive demo] | ||
|- | |- | ||
− | | | + | |14.10. |
− | |Supervised learning in feedforward neural networks (perceptrons), pattern classification, function approximation. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-fwdnn.4x.pdf slides] | + | |Supervised learning in feedforward neural networks (perceptrons), pattern classification, function approximation. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-fwdnn.4x.pdf slides] |
|R&N (2010), ch.18.2; Marsland (2009), ch.2-3, Engelbrecht (2007), ch.2-3 | |R&N (2010), ch.18.2; Marsland (2009), ch.2-3, Engelbrecht (2007), ch.2-3 | ||
|- | |- | ||
− | | | + | |21.10. |
− | |Unsupervised (self-organizing) neural networks: feature extraction, data visualization. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-unsup.4x.pdf slides] | + | |Unsupervised (self-organizing) neural networks: feature extraction, data visualization. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-unsup.4x.pdf slides] |
|Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4 | |Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4 | ||
|- | |- | ||
− | | | + | |28.10. |
− | | | + | |Statistical learning, probabilistic models. [http://dai.fmph.uniba.sk/courses/ICI/References/ci-prob.4x.pdf slides] |
− | | | + | |R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2 |
|- | |- | ||
− | | | + | |04.11. |
|Interim summary (review) | |Interim summary (review) | ||
− | | | + | |Thursday: mid-term test |
− | + | ||
− | + | ||
− | + | ||
− | + | ||
|- | |- | ||
− | | | + | |11.11. |
− | |Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. | + | |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. | ||
|- | |- | ||
− | | | + | |18.11. |
|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; Woergoetter & Porr (2008). | |R&N (2010), ch.21.3-5; Woergoetter & Porr (2008). | ||
|- | |- | ||
− | | | + | |25.11. |
− | |Evolutionary computation: basic concepts, genetic algorithms. | + | |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 | ||
|- | |- | ||
− | | | + | |03.12. |
− | |Fuzzy systems, fuzzy logic and reasoning. | + | |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) | ||
|} | |} | ||
+ | Note: Dates refer to lectures, seminars will be on day+3 each week. | ||
== References == | == References == | ||
Riadok 109: | Riadok 105: | ||
== Course grading == | == Course grading == | ||
− | * Active participation during the seminar/exercise: 5 for lectures, 10 for exercises (max. 15 points) | + | * Active participation during the seminar/exercise: 5 for lectures, 10 for exercises (max. 15 points). |
− | * Written mid-term test (max. | + | * Written mid-term test (max. 15 points). |
− | + | * Final written-oral exam (max. 20 points, 4 questions). Minimum of 7 points required. | |
− | * Final written-oral exam (max. | + | * Optional: small final project (max. 5 points) = 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. |
* <b>Overall grading:</b> A (50-46), B (45-41), C (40-36), D (35-31), E (30-26), Fx (25-0). | * <b>Overall grading:</b> A (50-46), B (45-41), C (40-36), D (35-31), E (30-26), Fx (25-0). |
Verzia zo dňa a času 17:16, 9. december 2019
Introduction to Computational Intelligence 2-IKV-115
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.
News
Exam: The list of questions is here. You will choose three questions (pseudo)randomly.
Course schedule
Type | Day | Time | Room | Lecturer |
---|---|---|---|---|
Lecture | Monday | 9:00 - 10:30 | I-9 | Igor Farkaš |
Seminar | Thursday | 14:00 - 15:30 | I-9 | Igor Farkaš & Endre Hamerlik |
Syllabus
Date | Topic | References |
---|---|---|
23.09. | What is computational intelligence, basic concepts, relation to artificial intelligence. slides | Craenen & Eiben (2003); wikipedia; R&N (2010), chap.1; Sloman (2002) |
30.09. | Taxonomy of artificial agents, nature of environments. slides | R&N (2010), chap.2 |
07.10. | Inductive learning via observations, decision trees. Model selection. slides | R&N (2010), ch.18.1-3,18.6; Marsland (2009), ch.6.1-2, visualization, interactive demo |
14.10. | Supervised learning in feedforward neural networks (perceptrons), pattern classification, function approximation. slides | R&N (2010), ch.18.2; Marsland (2009), ch.2-3, Engelbrecht (2007), ch.2-3 |
21.10. | Unsupervised (self-organizing) neural networks: feature extraction, data visualization. slides | Marsland (2009), ch.9-10, Engelbrecht (2007), ch.4 |
28.10. | Statistical learning, probabilistic models. slides | R&N (2010), ch.13,20.1-2; Marsland (2009), ch.8.1-2 |
04.11. | Interim summary (review) | Thursday: mid-term test |
11.11. | Reinforcement learning I: basic principles and learning methods (TD-learning). Prediction problem. slides | R&N (2010), ch.21.1-2. |
18.11. | Reinforcement learning II (Q, SARSA), actor-critic, control problem, RL for continuous domains. | R&N (2010), ch.21.3-5; Woergoetter & Porr (2008). |
25.11. | Evolutionary computation: basic concepts, genetic algorithms. slides | Engelbrecht (2007), ch.8 |
03.12. | 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.
- Sloman A. (2002). The Irrelevance of Turing Machines to AI. In Scheutz M. (ed.): Computationalism: New Directions, MIT Press, Cambridge, MA, pp. 87–127.
- Woergoetter F., Porr B. (2008). Reinforcement learning, Scholarpedia, 3(3):1448.
- Zadeh L. (2007). Fuzzy logic, Scholarpedia, 3(3):1766.
Course grading
- Active participation during the seminar/exercise: 5 for lectures, 10 for exercises (max. 15 points).
- Written mid-term test (max. 15 points).
- Final written-oral exam (max. 20 points, 4 questions). Minimum of 7 points required.
- Optional: small final project (max. 5 points) = 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.
- Overall grading: A (50-46), B (45-41), C (40-36), D (35-31), E (30-26), Fx (25-0).