Due to a reorganisation we are not able anymore to maintain these files. They will be removed in the near future.

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Educational resources

To understand the problems in the Pattern Recognition area you need to have an imagination, intuition as well as some knowledge and understanding of basic mathematics. To find the solutions or to design algorithms, you need to know more. Below, you will find a number of references to glossaries, books or courses online. They should help you in understanding approaches to Pattern Recognition as well as the more specific areas in its neighboorhood.

• An extensive glossary of Machine Learning
• A Statistical Learning/Pattern Recognition Glossary by Thomas Minka
• BriefBook An extended glossary, covering subjects in statistics, computing, analysis and related fields
• Engineering Statistics Handbook
• Eric's Weisstein world of mathematics Concise encyclopedia of mathemtics
• Eric's Weisstein world of physics Concise encyclopedia of physics
• Global index An index of terms from algebra, physics, signal/image processing, etc; by Julius Orion Smith III
• Internet Mathematics Library
• Introductory statistics: concepts, models, and applications by David W. Stockburger
• Linear algebra with applications by Toomas Lepikult (very useful)
• Mathematical Programming Glossary
• OneLook dictionary It serches many dictionaries at once
• SurfStat glossary
• Principia Cybernetica Web
• Web Dictionary of Cybernetics and Systems

Probability, Statistics, Math
• Analysis WebNotes by John Lindsay Orr
• Concepts and Applications of Inferential Statistics An interactive textbook by Richard Lowry
• HyperStat online textbook A statistics textbook by David M. Lane
• Introduction to probability A complete book by J. Laurie Snell
• Probability Tutorials by Noel Vaillant; quite equipped
• Statistical Computation A textbook by Richard Lowry
• Virtual Laboratories in Probability and Statistics by Kyle Siegrist

Pattern Learning issues

• Information Theory, Inference and Learning Algorithms An excellent textbook by David MacKay
• Neural and Adaptive Systems: Fundamentals Through Simulations An Interactive Neural Network Book by Principe, Euliano, and Lefebvre
• Introduction to Machine Learning by Nils J. Nilsson
• Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
• Speech Analysis by Tony Robinson
• Wavelets, Approximation and Statistical Applications Online book by Härdle, Kerkyacharian, Picard and Tsybakov

• Advanced Statistics & Optimization
• An Interactive Image Processing Course at TU Delft
• A Short Course in Information Theory by David J.C. MacKay
• Digital Image Processing Course
• Information retrieval a textbook by C. J. van Rijsbergen
• Introductory Probability A course by Susan P. Holmes
• Machine Learning by Tom Mitchell
• Machine Learning theory course for students
• Machine Learning and Pattern Recognition course (lectures)
• Pattern Recognition A course by Frigui Hichem
• Statistical Pattern Recognition for students
• Web Pattern Recognition Course by Godfried Toussaint

• Delft Courses on Pattern Recognition