Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Statistical Analysis with Missing Data

http://bcpw.bg.pw.edu.pl/Content/1968/Rudiments.pdf

Rudiments of rough sets

General Topology-I

Rough Sets in Knowledge Discovery 2

Rough mereology: A new paradigm for approximate reasoning

1 Rough Set Theory: An Introduction.- 2 The Sentential Calculus.- 3 Logical Theory of Approximations.- 4 Many-Valued Sentential Calculi.- 5 Propositional Modal Logic.- 6 Set Theory.- 7 Topological Structures.- 8 Algebraic Structures.- 9 Predicate Calculus.- 10 Independence, Approximation.- 11 Topology of Rough Sets.- 12 Algebra and Logic of Rough Sets.- 13 Infinite-valued Logical Calculi.- 14 From Rough to Fuzzy.- List of Symbols.

Rough Sets: Mathematical Foundations

Z. Pawlak: Foreword.- Introduction: L. Polkowski, A. Skowron: Introducing the Book Z. Pawlak: Rough Set Elements L. Polkowski, A. Skowron: Rough Sets: A Perspective.- Foundations: G. Cattaneo: Abstract Approximation Spaces for Rough Theories S. Demri, E. Orlowska: Complementarity Relations: Reduction of Decision Rules and Informational Representability T.Y. Lin: Granular Computing on Binary Relations I. Data Mining and Neighborhood Systems T.Y. Lin: Granular Computing II. Rough Set Representations and Belief Functions S. Miyamaoto: Fuzzy Multisets and a Rough Approximation by Multiset-Valued Function M. Moshkov: On Time Complexity of Decision Trees A. Nakamura: Graded Modalities in Rough Logic P. Pagliani: A Practical Introduction to the Model-Relational Approach to Approximation Spaces E. SanJuan, L. Iturrioz: Duality and Information Representability of some Information Algebras J. Stepaniuk: Rough Relations and Logics A. Wasilewska, L. Vigneron: Rough Algebras and Automated Deduction S.K.M. Wong: A Rough-Set Model for Reasoning about Knowledge Y.Y. Yao: Generalized Rough Set Models.- Methods and Applications: J.G. Bazan: A Comparison of Dynamic and Non-Dynamic Rough Set Methods for Extracting Laws from Decision Tables J.W. Grzymala-Busse: Applications of the Rule Induction Systems LERS A. Ohrn, J. Komorowski, A. Skowron, P. Synak: The Design and Implementation of a Knowledge Discovery Toolkit Based on Rough Sets - The ROSETTA System W. Kowalczyk: Rough Data Modelling: a New Technique for Analyzing Data M. Kryszkiewicz: Properties of Incomplete Information Systems in the Framework of Rough Sets H. Son Nguyen, S. Hoa Nguyen: Discretization Methods in Data Mining Z. Piasta, A. Lenarcik: Learning Rough Classifiers from Large Databases with Missing Values J. Stefanowski: On Rough Set Based Approaches to Induction of Decision Rules R. Susmaga: Experiments in Incremental Computation of Reducts W. Ziarko: Rough Sets as a Methodology for Data Mining.

Methodology and applications

1. Introduction.- Introducing the Book.- 1. A Rough Set Perspective on Knowledge Discovery in Information Systems: An Essay on the Topic of the Book.- 2. Methods and Applications: Reducts, Similarity, Mereology.- 2. Rough Set Algorithms in Classification Problem.- 3. Rough Mereology in Information Systems. A Case Study: Qualitative Spatial Reasoning.- 4. Knowledge Discovery by Application of Rough Set Models.- 5. Various Approaches to Reasoning with Frequency Based Decision Reducts: A Survey.- 3. Methods and Applications: Regular Pattern Extraction, Concurrency.- 6. Regularity Analysis and its Applications in Data Mining.- 7. Rough Set Methods for the Synthesis and Analysis of Concurrent Processes.- 4. Methods and Applications: Algebraic and Statistical Aspects, Conflicts, Incompleteness.- 8. Conflict Analysis.- 9. Logical and Algebraic Techniques for Rough Set Data Analysis.- 10. Statistical Techniques for Rough Set Data Analysis.- 11. Data Mining in Incomplete Information Systems from Rough Set Perspective.- 5. Afterword.- 12. Rough Sets and Rough Logic: A KDD Perspective.- Appendix: Selected Bibliofgraphy on Rough Sets.

Lech Polkowski

Papers

Rough Sets in Knowledge Discovery 2

Rough mereology: A new paradigm for approximate reasoning

Rough Sets: Mathematical Foundations

Methodology and applications

Rough set methods and applications: new developments in knowledge discovery in information systems