Machine Learning Classic Books

Starter Book List

2. The beauty of mathematics PDF

   The author Wu Everyone is familiar with it. The application of mathematics in the fields of machine learning and natural language processing is described in a very popular language.

4. "Programming Collective Intelligence" ("collective Wisdom Programming") PDF

   Author Toby Segaran is also the author of Beautifuldata:the Stories Behind Elegant Data Solutions (the beauty of data: The story behind the decryption of elegant data solutions). The greatest advantage of this book is that there is no theoretical derivation and complex mathematical formulas, it is a very good introductory book. At present, the Chinese version has been out of stock, for those who are interested in this field, the English PDF is a good choice, because there are a lot of classic book translation is poor, can only read English version, it is better to start from this. Also, this book is suitable for quick reading, because it is commented that after reading some classical books with mathematical deduction, the book will find nothing to say, just give a lot of examples.

6. Algorithms of the Intelligent Web (Smart Web algorithm) PDF

   Author Haralambos Marmanis, Dmitry Babenko. The formula in this book is a little bit more than "collective intelligence programming", the example of which is mostly the application on the Internet, to see the name. The disadvantage is that the matching code inside is BeanShell and not python or anything else. In general, this book is still suitable for beginners, and the same need to read the same as the previous one, if you finish reading the previous one, this one can not have to look at the code, understand the main idea of the algorithm is OK.

8. Statistical learning Method PDF

   Author Hangyuan Li, is one of several people in the field of machine learning in China, has been a senior researcher at MSRA, now Huawei Noah's Ark laboratory. The book wrote 10 algorithms, each of the introduction of the algorithm is very straightforward, directly on the formula, is downright "dry book." The references at the end of each chapter are also convenient for children's shoes that want to understand the algorithm directly to the classic paper; This book can be used as a supplementary reading for each of the two books.

10. "Machine learning" (ml) PDF

    Author Tom Mitchell is a master of CMU, with a machine learning and semi-supervised learning Network course video. This book is a good book for translation in the field, and the algorithms are much larger than the range of statistical learning methods. It is commented that the book is mainly about inspiration, explain why the formula was founded rather than derivation; But some basic classics are still not outdated, so this book is now almost a must-read for machine learning.

12. "Mining of Massive Datasets" ("Big Data") PDF

    Author Anand rajaraman[3], Jeffrey David Ullman,anand is the PhD of Stanford. This book introduces a lot of algorithms, and also describes these algorithms in the data size is larger than the time of the deformation. But in space, each algorithm does not have a sense of unfolding, if you want to know more about the need to look at other information, but then the algorithm is enough to understand. There is also a little bit of a lack of the original and translation of the book have many errors, errata longer, the reader must attentively.

14. Mining:practical machine learning Tools and techniques (Data mining: Utility learning Technology) PDF

    Author Ian H. Witten, Eibe Frank is the author of Weka and a professor at the University of Waikato in New Zealand. Their "managinggigabytes" [4] is also a classic book on information retrieval. The most characteristic of this book is the introduction of the use of Weka, but its theoretical part is too thin, as an introductory book can also, but the classic introductory books such as "collective Wisdom Programming", "Intelligent Web Algorithm" has been very classic, learning words should not read too many introductory books, suggest only to see some of the above two books did not talk about algorithms.

16. "Machine learning and its application"

    Zhou Zhihua, Prof Qiang Yang editor. From the "machine learning and its Application seminar" Anthology. The seminar was initiated by the Fudan University's Intelligent Information Processing laboratory, which has been held for 10 sessions, and domestic Daniel such as Hangyuan Li, Xiangliang, Wang Haifeng, tie and Kaiyu have lectured at the conference. This book speaks of a lot of machine learning at the forefront of specific applications, need to have a basic ability to understand. If you want to learn about machine learning trends, you can browse the book. Academic conferences in the area of interest are the way to discover research trends.

18. "Managing Gigabytes" (Deep search engine) PDF

    A good book for information retrieval.

20. "Modern Information Retrieval" PDF

    Ricardo Baeza-yates et al. 1999. It looks like the first book that covers IR completely. Unfortunately, IR has progressed rapidly over the years, and the book is somewhat outdated. It's a good idea to turn over and do a reference. In addition, Ricardo is now the head of Yahoo! for Europe and Latin Ameria.

22. Recommended Systems Practice PDF

    Xiangliang, good introductory reading.

Depth

2. Pattern Classification (second edition of Model classification) PDF

   Author Richard O Duda[5], Peter E. Hart, David. The foundation of pattern recognition, but the better method of SVM and boosting method is not introduced in the recent dominant position, and is evaluated as "exhaustive suspicion".

4. "Pattern Recognition and machine learning" PDF

   Author Christopher M. Bishop[6], abbreviated to PRML, focuses on probabilistic models, is a Bayesian method of the tripod, according to the evaluation "with a strong engineering breath, can cooperate with Stanford University Andrew Ng's machine learning Video tutorial to learn together, the effect of doubling. ”

6. The Elements of statistical learning:data Mining, Inference, andprediction, (Statistical Learning Fundamentals: Data Mining, reasoning and Forecasting, second edition) PDF

   Author Roberttibshirani, Trevor Hastie, Jerome Friedman. The author of this book is the most active researcher of the boosting method, and the invention of gradient boosting presents a new perspective of understanding boosting method, greatly extending the scope of application of the boosting method. This book is a more comprehensive introduction to the most popular methods, and it may be a little more useful to the engineering staff. On the other hand, it not only summarizes some of the technologies that have matured, but also has a brief discussion on some of the issues that are still being developed. Let the reader fully realize that machine learning is a still very active field of research, it should make academic researchers often read often new feelings. "[7]

8. "Data mining:concepts Andtechniques" (the third edition of Mining: Concepts and Technologies) PDF

   Author (United States) Jiawei Han[8], (plus) Micheline Kamber, (plus) Jian Pei, of which the first author is Chinese. The book is no doubt the data mining aspects of the classic, but the translation is always sprayed, no way, most of the translated books are sprayed, want to eat other people chew things, learn English well.

10. "AI, Modern approach 2nd" PDF

    Peter Norvig, a undisputed field classic.

12. "Foundations of statistical Natural Language processing" PDF

    The field of natural language processing is a recognized classic.

14. "Information theory:inference and Learning algorithms" PDF

16. "Statistical Learning theory" PDF

    Vapnik's masterpiece, the authority of the statistical academia, this book to the theory to the philosophical level, his other book "The Nature Ofstatistical Learning theory" is also a rare statistical study of good books, but these two books are relatively deep, Suitable for readers with a certain foundation.

Fundamentals of Mathematics

2. Matrix Analysis PDF

   Roger Horn. The undisputed classical matrix analysis field

4. "Probability theory and its application" PDF

   William Feller. A very good book, but the math is too heavy for machine learning.

6. "All of Statistics" PDF scanned PDF HD version

   In this direction of machine learning, statistics are also very important. Recommend all of statistics, a very concise textbook for CMU, focusing on concepts, simplifying calculations, simplifying concepts and statistical content unrelated to machine learning, which can be said to be a good quick start material.

8. "Nonlinear programming, 2nd" PDF

   Optimization method, a reference book for nonlinear programming.

10. "Convex optimization" PDF companion code

    Boyd's classic books, cited more than 14,000 times, for practical applications, and have matching code, is a rare good book.

12. "Numerical optimization" PDF

    The second edition, Nocedal, is ideal for students and engineers of non-numeric majors, with clear and detailed algorithm flow and clear principles.

14. "Introduction to Mathematical Statistics" PDF

    Sixth edition, Hogg, this book introduces the basic concepts of probabilistic statistics and various distributions, as well as Ml,bayesian methods.

16. "An Introduction to probabilistic graphical Models" PDF

    Jordan, this book introduces the basic concepts of conditional independence, decomposition, blending and conditional blending, and also introduces the implicit variables (latent variables), and I believe that we have encountered this concept when we implement EM algorithm in the hidden Markov chain and the Gaussian mixed model.

18. "Probabilistic graphical Models-principles and techniques" PDF

    Koller, a very thick and comprehensive book, is highly theoretical and can be used as a reference book.

20. Specific Math PDF

    Classic

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2. Linear algebra (Linear Algebra):

   I think the students in the country will learn this course, but not every teacher can carry out its essentials. This discipline is necessary for learning, and it is essential for its thorough mastery. I studied this course when I was in the first year of Hkust, and then after I arrived in Hong Kong, I read the linear algebra again, reading

   Introduction to Linear Algebra (3rd Ed.) by Gilbert Strang.

   This book is the textbook used by MIT's Linear Algebra class, and is also a classic textbook chosen by many other universities. Its difficulty is moderate, the explanation is clear, it is important to discuss many core concepts more thoroughly. I personally think that learning linear algebra, the most important thing is not to be proficient in matrix operations and the equation of the method-these in the actual work of Matlab can do, the key is to deeply understand a few basic and important concepts: subspace (subspace), orthogonal (orthogonality), Eigenvalues and eigenvectors (eigenvalues and eigenvectors), and linear transformations (Linear transform). From my point of view, the quality of an online textbook is whether it can give sufficient attention to these fundamental concepts, and whether it can be made clear about their links. Strang's book is doing very well in this respect.

   Moreover, the book has a unique advantage. The author of the book Teaches linear algebra classes (18.06) in MIT for a long time, and the course video is available on the MIT Open Courseware website. Friends who have time can watch the video of the teacher's lectures while studying or reviewing the textbook.

   Http://ocw.mit.edu/OcwWeb/Mathematics/18-06Spring-2005/CourseHome/index.htm

4. Probability and statistics (probability and Statistics):

   There are many introductory textbooks for probability theory and statistics, and I don't have any special recommendations at the moment. What I want to introduce here is a basic textbook on multivariate statistics:

   Applied multivariate statistical analysis (5th Ed.) by Richard A. Johnson and Dean W. Wiche RN

   This book is for me to learn when I am in contact with vector statistics, and the basis for my research in Hong Kong is to lay it down. Some students in the lab also borrowed this book to learn vector statistics. This book does not specifically pursue the depth of mathematics, but in an easy-to-understand way to tell the main basic concepts, read very comfortable, the content is very practical. For linear regression, factor analysis, principal component Analysis (PCA), and canonical component Analysis (CCA) These learning The basic methods in this paper have also been discussed preliminarily. After

   , Bayesian statistics and graphical models can be further studied. An ideal book is

   Introduction to graphical Models (draft version). by M. Jordan and C. Bishop.

   I don't know if this book has been published (not to be confused with learning in graphical models, it's a collection of essays, not for beginners). This book from the basic Bayesian statistical model has been deep into the complex statistical network estimation and inference, in simple, statistical learning many important aspects of this book have a clear exposition and detailed explanation. Inside MIT you can access, as well as the outside, it seems to have an electronic version.

6. Analysis:

   I think everyone has studied calculus or mathematical analysis in college, and the depth and breadth vary with each school. This field is the foundation of many disciplines, and the recommended textbook is

   Principles of mathematical analysis, by Walter Rudin

   A bit old, but absolutely classic, deep and thorough. Disadvantage is more difficult-this is Rudin's book's consistent style, suitable for a certain basis to look back.

   In analyzing this direction, the next step is functional analysis (functional).

   Introductory functional analysis with applications, by Erwin Kreyszig.

   Suitable as the basic teaching material of the functional, easy to cut into without losing all-round. I especially like it. Special attention is paid to spectral theory and operator theory, which is particularly important for the study of learning. Rudin also has a book on functional analysis, the book may be more profound in mathematics, but not easy to get started, the content and learning the relevance of the book.

   In the analysis of this direction, there is an important subject is the measurement theory (Measure theory), but I have read the book is not yet felt particularly worthy of introduction.

8. Topology (topology):

   The basic topology books I have read have their own characteristics, but in general, I would most highly recommend:

   Topology (2nd Ed.) by James Munkres

   This book is the work of Professor Munkres's long-term coaching of the MIT topology class. There is a comprehensive introduction to general topology and a modest discussion of algebraic topologies (algebraic topology). This book does not need special mathematical knowledge can begin to learn, from the basic concept of set theory (many books disdain to say this) to Nagata-smirnov theorem and Tychonoff theorem and other deep theorems (many books avoid this) are covered. The narrative way is very strong, for many theorems, in addition to the proof process and guide you to think behind the principle of the context, a lot of amazing highlights-I often read to forget hunger, do not want to addictive. Many of the exercises are quite standard.

10. Manifold theory (manifold theory):

    For the topology and analysis of certain certainty, you can begin to learn the manifold theory, otherwise the study can only flow in the superficial. The book that I use is

    Introduction to Smooth manifolds. by John M. Lee

    Although the title has introduction the word, but in fact the book involved in very deep, in addition to teaching basic manifold, tangent space, bundles, sub-manifold, etc., also explored such as The theory (Category theory) , and some of the more advanced topics such as the Rham cohomology and the integration manifold. There are quite a lot of discussions about Lie groups and Li algebra. It is popular and rigorous, but it needs to be familiar with certain marking methods.

    Although Lie groups are based on the concept of smooth manifolds, it is possible to learn the Lie groups and Lie algebras directly from the matrix-a method that may be more practical for those who are in urgent need of solving problems with Lie groups. Moreover, it is beneficial to deepen understanding of a problem from different perspectives. The following book is an example of this direction:

    Lie Groups, Lie algebras, and Representations:an elementary Introduction. by Brian C. Hall

    The book starts from The Matrix and introduces the concept of the matrix Lie groups from the algebraic rather than the geometrical point of view. And the exponential mapping is established by defining the operation, and the Lie algebra is introduced in this way. This method is more acceptable than the traditional way of defining Lie algebra through the "Left Invariant vector field (left-invariant vector field)", and it is easier to reveal the meaning of the Lie algebra. Finally, there is a special discussion linking this new definition to the traditional way.

(turn) machine Learning Classic Books