Approximate reasoning 1000-1M00WA
1. Selected problems of approximate reasoning in such domains as machine learning, pattern recognition, data mining and knowledge discovery, soft computing, in particular, algorithmic methods in discovery of patterns, laws and approximate reasoning schemes from experimental data and domain knowledge based on non-conventional models of computation (e.g., evolutionary programming, neural networks, rough sets and Boolean reasoning, Bayesian networks, approximate reasoning schemes and granular computing).
2. Strategies in adaptive and autonomic computing, in particular, strategies for learning interactions.
3. Theoretical aspects of approximate reasoning: (i) Vapnik-Chervonenkis dimension and the role of different entropies in concept learning; (ii) logical foundations for approximate reasoning, in particular, in distributed (multiagent systems), non-monotonic reasoning and approximate reasoning from sensory data, reasoning about knowledge, belief revision, conflict analysis and negotiations; (iii) computational complexity of approximate reasoning: heuristics and approximation of NP-hard problems, minimal length principle and its relationship to Kolmogoroff complexity; (iv) selected methods of information compression.
Main fields of studies for MISMaP
mathematics
Type of course
Mode
Course coordinators
Assessment criteria
Final grade: 50% - oral exam (25% - topics covered by lectures and exercises, 25% - papers extending selected topics covered by lectures), 50% - preparing and delivering a presentation (during exercises) on the basis of selected research papers.
Bibliography
1. T. Baeck: Evolutionary Algorithms in Theory and Practice, Oxford University Press 1996.
2. J. Barwise, J. Seligman: Information flow: The logic of distributed systems, Cambridge University Press 1997.
3. T. Hastie, R. Tibshirani, J. Friedman: The elements of statistical learning. Data mining, inference, and prediction. (2nd edition), Springer 2009.
4. D.S. Hochbaum: Approximation algorithms for NP-hard problems. PWS 1997.
5. A. Skowron: Approximate reasoning, notes prepared for monographic lectures, 2013.
6. V. Vapnik: Statistical learning theory. Wiley 1998.
Additional information
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