I am looking for a book about machine learning that would suit my physics background. I am more or less familiar with classical and complex analysis, theory of probability, calculus of variations, matrix algebra, etc. However, I have not studied topology, measure theory, group theory, and other more advanced topics. I try to find a book that is written neither for beginners, nor for mathematicians.
Recently, I have read the great book "Statistical inference" written by Casella and Berger. They write in the introduction that "The purpose of this book is to build theoretical statistics (as different from mathematical statistics) from the first principles of probability theory". So, I am looking for some "theoretical books" about machine learning.
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There are many online courses and brilliant books out there that focus on the practical side of applying machine learning models and using the appropriate libraries. It seems to me that there are no problems with them, but I would like to find a book on theory.
For example, if I remember correctly, in my introductory course to machine learning, the professor suggested the book Pattern Recognition And Machine Learning (2006) by Bishop, although we never used it during the lessons. This is a good book, but, in my opinion, it covers many topics, such as variational inference or sampling methods, that are not suited for an introductory course.
The book Artificial Intelligence. A Modern Approach, by Norvig and Russell, definitely does not focus on machine learning, but it covers many other aspects of artificial intelligence, such as search, planning, knowledge representation, machine learning, robotics, natural language processing or computer vision. This is probably the book that you should read and use if you want to have an extensive overview of the AI field. Although I never fully read it, I often used it as a reference, as I use the other mentioned book. For instance, during my bachelor's and, more specifically, an introductory course to artificial intelligence, we had used this book as the reference book, but note that there are other books that provide an extensive overview of the AI field.
There are at least three other books that I think you should also be aware of, given that they also cover the actual theory of learning, aka (computational) learning theory, before diving into more specific topics, such as kernel methods.
Pattern Recognition And Machine Learning is a great theoretical book. I don't know anything better on standard ML. I read several pages from it myself and all my colleagues researchers suggest to look there if you are not sure about some concepts. The 2 problems with it are that it's huge and it doesn't cover almost all deep learning models known for today.
Actually, ML theory is more like probability theory and statistics. Especially, statistical learning theory (which is nothing more than probability theory and statistics). I haven't read any books on SLT so have a look at this answer.
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First of all, data only comes in so many forms that it might make sense to stick to a more "concrete definition". Data Science is necessarily practical. But here are a few other books with a more theoretical grounding. Others will certainly know many more...
Hastie et al is at the mathematical level you require - being written by statistics academics with strong mathematical pedigree (Hastie is currently a mathematics professor, for example) - and the complete text is available for free online via the authors' website. It is probably about the best general survey of machine learning for people with mathematical and statistical background at the graduate student level. That said, it is still a survey, and individual topics will require follow up elsewhere, though useful recommended reading is provided.
Russel & Norvig isn't about machine learning or data science, but rather the wider field of artificial intelligence, in which it includes machine learning as smallish subset, and data science effectively not at all. For example, it discusses a number of different kinds of systems of pre-programmed AI approaches - the exact opposite of machine learning. It is interesting if you want to understand the wider world of automation but will do little to help you understand ML.
These two texts combined are 2,000 pages total and cover everything from undergrad-level probability to linear models, and (as far as I can tell) everything covered by Elements of Statistical Learning, in addition to time series, probabilistic graphical models, deep learning, and Monte Carlo methods.
As I said my wife is printing all her university PDFs. A local company prints and binds them relatively cheaply and like that she has a physical book to highlight key points, make notes and probably most importantly not have to stare at a screen for hours on end. Obviously learning a computer program means staring at a screen for hours, but having a physical copy of information, does mean you can give the eyes a break (older generation!)
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No Experience Required is easy to read and to follow, and fun at times too (read and learn as you do the work). It is not very expensive and is readily (physically) available for perusing at some of the local bookstores, so you can look at it and feel it before you make a decision.
If we as a school were to be switching from Stine because its not an acceptable publisher of an ebook for the school - what other good books would you recommend. Are there any published by say..wiley? Also - what are you're thoughts of learning from ebooks rather than textbooks for revit?
I'd been advised by a REVIT consultant to use the Ascent fundamentals book as my learning tool. It is an autocad licensed group but at $90 + $15 shipping, it is not free. Which tutorial do you recommend for an experienced autocad user that wants to become proficient at revit?
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