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first, statistical learning

Statistical learning and machine learning exactly what difference, I have been confused! "Monash of Machine University" by Professor Claude Sammut of UNSW Australia and Australia Geoffrey Webb, encyclopedia ing "is discussed in the following:" Inductive learning is a subclass of the machine learning that studies algorithms for learning knowledge based on S Tatistical regularities. The learned knowledge typically has no deductive guarantees of correctness, though there could be statistical forms of Guara Ntees, they probably think: statistical learning is a subclass of machine learning.

Carnegie Mellon University's Professor Larry Wasserman in his blog Normal Deviate a blog "Statistics Versus machine Learning" said: "What Is the difference between these the other fields (Statistics and machine learning)? The short answer is:none. They is both concerned with the same question:how does we learn from data? "after answering without any distinction, Larry Wasserman also outlined two areas of a little different! In any case, these Daniel also have their own views! What ' s the difference? But it is really important? Let's busy with and more important things!

Be able to meet "the Elements of Statistical learning" This book to thank my daughter-in-law introduction, I remember in 2010 I have been working hard to complete a machine learning project, because this direction before the company has not been done, coupled with the lack of good reference and introductory books, So the progress is difficult. One of the chapters of this book has played a great role in the process of completing the whole project, when I wanted such a classic book, I must study it after the completion of the project, did not think, a drag is four years, now began slowly to understand the mystery!

Domestic photocopy cover unexpectedly translated as "the basis of statistical learning", in fact, this book is not the basis for a good understanding of the mystery, theoretical foundation is necessary, the individual think or translation for the "Essentials of statistical learning" more accurate point (but it is really important? Let's busy with and more important things!).

Find a lot of information, unexpectedly found the author Trevor Hastie and Robert Tibshirani unexpectedly still use this book as the blueprint, opened a statistical learning class, the course hangs in Stanford University online course official online! Master is a master, did not expect two old man unexpectedly will so earnest, the kind of share spirit of American University, let me admire for a long time!

Course Website:

2014 course

https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/
courseware/995220423fd14a4588d8e47920f1b5df/99faa3a82fca4fc19adc577ce9f75afd/

Or 2015 course

Https://class.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about

Reference Books (Need the original ebook can own Google or give me a message):

1, the Elements of statistical learning

by Trevor Hastie, Robert Tibshirani and Jerome H.friedman

http://www-bcf.usc.edu/~gareth/ISL/

2, anIntroduction to statistical learning

by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

http://statweb.stanford.edu/~tibs/ElemStatLearn/

3, modern multivariate statistical techniques

by Alan J. Izenman

PS: This book is more basic than the above two!

second, convex optimization

When I got Stephen Boyd's "Convex optimization" book, there was a brief encounter feeling. Mainly because when I see "the Elements of Statistical learning" a book, there is not understand the main view of some of the knowledge of operations research or on-line access to fragmented optimization theory, and finally hard to chew off part of the content. 2013 at the end of the work need to use similar "Minimum Volume enclosing ellipsoid" things, let me once again feel the need to take the time to systematically learn mathematical knowledge, especially convex optimization knowledge, and then found this course!

Course Website:

https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/
courseware/7206c57866504e83821d00b5d3f80793/

Reference Books (Need the original ebook can own Google or give me a message):

1, convex optimization

by Stephen Boyd and lieven Vandenberghe

http://web.stanford.edu/~boyd/cvxbook/

three, Fourier transform and its application

http://see.stanford.edu/see/courseInfo.aspx?coll=84d174c2-d74f-493d-92ae-c3f45c0ee091

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