IAPR

Book lists for machine learning


Online Books

  1. Advances in large margin classifiers, B.Schoelkopf, and C.Schuurmans, MIT Press, Cambridge, MA, 2000
  2. Convex Optimization, Stephen Boyd and Lieven Vandenberghe Cambridge University Press, 2004
  3. Expert Systems and Probabilistic Network Models , E. Castillo, J.M. Gutiérrez, and A.S. Hadi, Springer-Verlag, 1997, ISBN-10: 0387948589, ISBN-13: 978-0387948584, Spanish version available online
  4. Information Theory, Inference, and Learning Algorithms, D. J.C. MacKay, Cambridge University Press, 2003, ISBN-13: 9780521642989.
  5. Introduction to Machine Learning, Draft of Incomplete Notes , Nils J. Nilsson, 1996
  6. Learning to Learn, Sebastian Thrun and Lorien Y. Pratt, Kluwer Academic Publishers, ISBN-10: 0792380479
  7. Bayesian Reasoning and Machine Learning (Draft), David Barber, 2010
  8. Machine Learning, Neural and Statistical Classification , D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds), 1994, ISBN-10: 013106360X, ISBN-13: 978-0131063600
  9. Markov Random Fields and Their Applications , Ross Kindermann and J. Laurie Snell, 1980, AMS ISBN: 0-8218-3381-2
  10. Neural Nets , Kevin Gurney.
  11. Probability Theory: the Logic of Science , E. T. Jaynes, Cambridge University Press, 2003, ISBN-10: 0521592712, ISBN-13: 978-0521592710
  12. Recent Advances in Robot Learning, Judy A. Franklin, Tom M. Mitchell, and Sebastian Thrun (Editors), Springer, 1996, ISBN-10: 0792397452
  13. Reinforcement Learning: An introduction , Richard Sutton and Andrew Barto, MIT Press, 1998, ISBN-10: 0-262-19398-1, ISBN-13: 978-0-262-19398-6

Book Support Sites

  1. Advances in Kernel Methods - Support Vector Learning, B.Schoelkopf, C.J.C. Burges and A.J. Smola, MIT Press, Cambridge, MA, 1999
  2. All of Statistics. A Concise Course in Statistical Inference, Larry Wasserman, Springer 2004
  3. An Introduction to Computational Learning Theory, Michael J. Kearns and Umesh V. Vazirani, MIT Press, 1994, ISBN-10: 0-262-11193-4, ISBN-13: 978-0-262-11193-5
  4. Bayesian Inference in Statistical Analysis, George E. P. Box and George C. Tiao, Wiley, 1992, ISBN: 978-0-471-57428-6
  5. Bayesian methods for nonlinear classification and regression , David Denison, Chris Holmes, Bani Mallick and Adrian Smith, Wiley, 2002, ISBN: 978-0-471-49036-4
  6. Bayesian Networks and Decision Graphs , Finn V. Jensen, Springer-Verlag, 2001, ISBN:0387952594
  7. Bayesian Theory , José M. Bernardo and Adrian F. M. Smith, Wiley, 2000, ISBN: 978-0-471-49464-5
  8. Bioinformatics: The Machine Learning Approach, Pierre Baldi and Søren Brunak, MIT Press, 1998, ISBN-10: 0-262-02442-X, ISBN-13: 978-0-262-02442-6
  9. Causality: Models, reasoning and Inference, Judea Pearl, Cambridge University Press, 2000, ISBN-10: 0521773628
  10. Computational Intelligence: A Logical Approach , David Poole, Alan Mackworth, Randy Goebel, Oxford University Press, New York 1998, ISBN: 0195102703
  11. Computer Manual in MATLAB to accompany Pattern Classification, 2nd ed. , David G. Stork and Elad Yom-Tov, John Wiley & Sons, 2004, ISBN: 0-471-42977-5
  12. Data Mining: Concepts and Techniques, 2nd ed. , Jiawei Han and Micheline Kamber, The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor, Morgan Kaufmann Publishers, 2006, ISBN 1-55860-901-6
  13. Data Mining: Practical Machine Learning Tools and Techniques (Second Edition) , Ian H. Witten and Eibe Frank, Morgan Kaufmann, 2005, ISBN 0-12-088407-0
  14. Elements of Information Theory, 2nd ed. , Thomas M. Cover and Joy A. Thomas, Wiley, 2006, ISBN: 0-471-24195-4
  15. Estimation of Dependences Based on Empirical Data, V. Vapnik, Springer Verlag, 2006, 2nd edition, Hardcover ISBN: 978-0-387-30865-4
  16. Gaussian Processes for Machine Learning , Carl Edward Rasmussen and Christopher K. I. Williams, The MIT Press, 2006, ISBN 0-262-18253-X.
  17. Introduction to Algorithms 2nd ed., Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Cliff Stein, MIT Press, 2001
  18. Introduction to AI Robotics , Robin Murphy, MIT Press, 2000, ISBN-10: 0-262-13383-0
  19. Introduction to Graphical Modelling, D Edwards, 2nd ed., Springer-Verlag 2000, New York, 333 pp. Hardcover ISBN 0-387-95054-0
  20. Introduction to Machine Learning, Ethem Alpaydin, The MIT Press, October 2004, ISBN 0-262-01211-1
  21. Kernel Methods for Pattern Analysis, J. Shawe-Taylor and N. Cristianini, Cambridge University Press, 2004, Hardback (ISBN-13: 9780521813976 | ISBN-10: 0521813972), Also available in eBook format
  22. Latent Variable Models and factor Analysis, 2nd ed. , David Bartholomew and Martin Knott, Hodder Arnold,1999, Hardback, ISBN-10: 0340 69243X, ISBN-13: 978-0340692431
  23. Learning in Graphical Models , Michael I. Jordan, The MIT Press, Nov 1998, ISBN 0262600323
  24. Learning Kernel Classifiers, Ralf Herbrich, The MIT Press, 2002, ISBN: 0-262-08306-X
  25. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond , Bernhard Schölkopf and Alexander J. Smola, The MIT Press, 2001, ISBN-10: 0262194759, ISBN-13: 978-0262194754
  26. Least Squares Support Vector Machines , J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, World Scientific Pub. Co., Singapore, 2002, ISBN 981-238-151-1
  27. Machine Learning , Tom Mitchell, McGraw-Hill, 1997, ISBN: 0070428077
  28. Monte Carlo Methods in Bayesian Computation , Ming-Hui Chen, Qi-Man Shao, and Joseph G. Ibrahim, Springer-Verlag, 2000, ISBN 0-387-98935-8
  29. Neural Networks for Pattern Recognition , Christopher Bishop, Oxford University Press, 1996, ISBN 0-19-853849-9 Hardback, ISBN 0-19-853864-2 Paperback
  30. Neurocomputing: Foundations of Research, James Anderson and Edward Rosenfeld (eds), MIT Press, 1988, ISBN-10: 0-262-51048-0, ISBN-13: 978-0-262-51048-6
  31. Pattern Classification, 2nd ed. , Richard Duda, Peter Hart and David Stork , John Wiley & Sons, 2001, ISBN: 0-471-05669-3
  32. Pattern Recognition and Machine Learning , Christopher M. Bishop, Springer, 2006, ISBN: 978-0-387-31073-2
  33. Pattern Recognition for Neural Networks , Brian Ripley, Cambridge University Press, 2008, ISBN 978-0-521-71770-0.
  34. Relational Data Mining, Saso Dzeroski and Nada Lavrac (editors), Springer, Berlin, 2001, ISBN-10: 3540422897
  35. Statistical Decision Theory and Bayesian Analysis, James O Berger, Springer, 1985 2nd ed., Hardcover ISBN: 978-0-387-96098-2
  36. Statistical Inference, G. Casella and R. Berger, Duxbury, 2001
  37. Support Vector Machines, John Shawe-Taylor & Nello Cristianini - Cambridge University Press, 2000
  38. Systems That Learn, 2nd Edition , Sanjay Jain, Daniel Osherson, James S. Royer, Arun Sharma, MIT Press, 1999, ISBN 0-262-10077-0
  39. The Elements of Statistical Learning: Data Mining, Inference, and Prediction,Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer-Verlag 2001

Other Books

  1. Advances in Learning Theory: Methods, Models and Applications, J.A.K. Suykens, G. Horvath, S. Basu, C. Micchelli, J. Vandewalle (Eds.), 2003, ISBN: 1 58603 341 7
  2. AI Application Programming, M. Tim Jones, Charles River Media, 2005, ISBN: 1584504218
  3. Applied Evolutionary Algorithms in Java, Robert Ghanea-Hercock, Springer, 2003, ISBN: 0387955682
  4. Artificial Intelligence, Rob Callan, Palgrave Macmillan, 2003, ISBN: 0333801369
  5. Bayesian Learning in Neural Networks, R. Neal, Springer-Verlag, 1996
  6. A Compendium of Machine Learning, Terry Caelli and Garry Briscoe, Intellect Books, 1996, ISBN-10: 1567501796
  7. Computational Learning Theory and Natural Learning Systems, Vol. IV: Making Learning Systems Practical, Russell Greiner, Thomas Petsche, Stephen Jose (Editors), The MIT Press, 1997, ISBN-10: 0262571188
  8. Construction and Assessment of Classification Rules, David J. Hand, John Wiley and Sons, 1997, ISBN 0-471-96583-9
  9. Data Mining and Knowledge Discovery with Evolutionary Algorithms, Alex A. Freitas, Springer, 2002, ISBN: 3-540-43331-7
  10. Elements of Machine Learning, Pat Langley, Morgan Kaufmann, 1995, ISBN-10: 1558603018
  11. Evolutionary Algorithms for Single and Multicriteria Design Optimization, Andrzej Osyczka, Physica-Verlag Heidelberg, 2001, ISBN-10: 3790814180
  12. Explanation-Based Neural Network Learning: A Lifelong Learning Approach, Sebastian Thrun, Kluwer Academic Publishers, 1996, ISBN-10: 0792397169
  13. Feature Extraction, Construction and Selection: A Data Mining Perspective, Huan Liu (Editor), Hiroshi Motoda (Editor), Springer, 1998, ISBN-10: 0792381963
  14. Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, Nikola K. Kasabov, The MIT Press, 1996, ISBN-10: 0262112124
  15. Genetic Algorithms in Search, Optimization, and Machine Learning, David E. Goldberg, Addison-Wesley, 1989, ISBN-10: 0201157675
  16. Graphical models for machine learning and digital communication, B. J. Frey, MIT Press, 1998
  17. Hidden Markov Models: Estimation and Control, Robert J. Elliott, Lakhdar Aggoun and John B. Moore, Springer, 1995
  18. An Introduction To Genetic Algorithms, Melanie Mitchell, MIT Press, 1998, 0-262-63185-7
  19. An Introduction to Kolmogorov Complexity and Its Applications, Ming Li and Paul Vitanyi, Second Edition, Springer Verlag,1997, ISBN 0-387-94868-6
  20. An Introduction to Latent Variable Models , Brian S. Everitt, Chapman & Hall, 1984, ISBN-10: 0412253100, ISBN-13: 978-0412253102
  21. Introduction to Statistical Pattern Recognition, 2nd ed, Keinosuke Fukunaga, Academic Press, 1990
  22. Learning in Neural Networks : Theoretical Foundations, M. Anthony and P. Bartlett, Cambridge University Press, 1999
  23. Machine Learning: A Theoretical Approach, Balas K. Natarajan, Morgan Kaufmann, 1991, ISBN-10: 1558601481
  24. Machine Learning and Data Mining: Methods and Applications, Ryszad S. Michalski (Editor), Ivan Bratko (Editor), Miroslav Kubat (Editor), John Wiley & Sons, 1998, ISBN-10: 0471971995
  25. Machine Learning and Image Interpretation, Terry Caelli, Walter F. Bischof, Springer, 1997, ISBN-10: 030645761X
  26. Machine Learning Methods for Planning, Steven Minton, Morgan Kaufmann, 1993, ISBN-10: 1558602488
  27. The Mathematics of Generalization, David H. Wolpert , Addison Wesley Longman, 1995, ISBN-10: 0201409852
  28. Multidimensional Scaling, 2nd ed, T.F. Cox and M. A. A. Cox, Chapman and Hall, 2000.
  29. The Nature of Statistical Learning Theory, V.N. Vapnik, second ed. Springer Verlag, 1999
  30. Nonlinear Programming, O. L. Mangasarian, SIAM, 1994
  31. Practical methods of Optimization, R. Fletcher, Wiley, 1988
  32. Probabilistic Networks and Expert Systems. Robert G. Cowell, Steffen L. Lauritzen and David J. Spiegelhater, Springer, 2005, Language: English, ISBN-10: 0387987673, ISBN-13: 978-0387987675
  33. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Judea Pearl, Morgan Kaufmann, 1988, ISBN-10: 1558604790
  34. Readings in Machine Learning, Jude Shavlik (Editor), Thomas Dietterich (Editor), Morgan Kaufmann, 1990, ISBN-10: 1558601430
  35. Reasoning about Uncertainty, Joseph Y. Halpern, The MIT Press, 2005, ISBN-10: 0262582597
  36. Recent Advances in Reinforcement Learning, Leslie Pack Kaelbling (Editor), Kluwer Academic Publishers, 1996, ISBN-10: 0792397053
  37. A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems, M. Vidyasagar, Springer-Verlag New York, Inc., Secaucus, NJ, 1997
  38. Theory of Probability: A Critical Introductory Treatment, Bruno de Finetti, Wiley and Sons, 1970

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