書籍 †
機械学習や統計関係の書籍をみなさんに紹介しましょう．
リンク †
- BayesFun の書評
(BayesFun:BookReview/和書, BayesFun:BookReview/洋書)
- Learning about Machine Learning, 2nd Ed @ Measuring Measures：機械学習と関連分野の代表的教科書のリスト
- 無料で読める書籍
- 統計科学のための電子図書システム
- XploRe e-books：統計関係の書籍のアーカイブ
- Gutenberg Project 無料で読める本のアーカイブ
- R.S.Sutton and A.G.Barto "Reinforcement Learning: An Introduction"：強化学習の定番教科書
- C.J.van Rijsbeergen, "Information Retrieval"
- Dan Sloughter, "Difference Equations to Differential Equations"
- N.Lavrac & S.Dzeroski, "Inductive Logic Programming -- Techniques and Applications"
- N.J.Nilsson, "Introduction to Machine Learning"
- D.Michie, D.J.Spiegelhalter, C.C.Taylor "Machine Learning, Neural and Statistical Classification
- A.N.Kolmogorov "Foundations fo the Theory of Probability"
- Raul Rojas "Neural Networks - A Systematic Introduction"
- Stephen Boyd and Lieven Vandenberghe "Convex Optimization"
- Thomas Weise "Global Optimization Algorithms - Theory and Application"
- Stan Z. Li "Markov Random Field Modeling in Computer Vision"
- R.M.Gray "Entropy and Information Theory"
- A.Hyvärinen et al. "Natural Image Statistics --- A probabilistic approach to early computational vision"
- D.Pollard "Convergence of Stochastic Processes"
- Richard Szeliski "Computer Vision: Algorithms and Applications"
- N.Nisan, T.Roughgarden, É.Tardos, and V.V.Vazirani "Algorithmic Game Theory"
- Anand Rajaraman and Jeffrey D. Ullman, Mining of Massive Datasets
- Jason Brownlee "Clever Algorithms: Nature-Inspired Programming Recipes"
- David Barber "Bayesian Reasoning and Machine Learning"
- 日本統計学会創立75周年記念出版『21世紀の統計科学』(全3巻) 増補HP版
書評一覧 †
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