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[Sourabh Doherty]

Iam 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, сcalculus 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.

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.

It looks very nice. The only point of concern is that the book was published in 2006. So, I am not sure about the relevance of the chapters considering neural nets, since this field is developing rather fast.

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.

Now it is difficult to evaluate if they would fit my needs because only a few pages are generally available online. However my first impression is that they do not. In the appendices of Artificial Intelligence: A Modern Approach I can read:

I'm looking for a book which assumes the reader has a good understanding in set theory, abstract algebra, measure and probability theory, statistics, topology, calculus, graph theory, complexity theory, etc and a preference for formal and axiomatic explanations rather than lengthy and so-called "intuitive" approaches based on basic mathematical objects and examples. Furthermore I don't want something that looks like a recipe book from the very beginning. I want a book that formalizes the abstract and common shape of all data science methods as well as their common aim first. Only after that it can start to explain the different categories by explicitly stating which further hypotheses each category is assuming and which cases/problems/domains they are known to handle efficiently or not.

At last, to be clear, I have no problem with being shown concrete examples and their treatment via a specific programming language for example. I just want this to come second as an illustration for the conceptual explanation, not as a substitute.

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.

Bishop also assumes a reasonable degree of mathematical maturity, although the table of contents may make the content appear more simple than it is, for example by listing a review of probability distributions including the Gaussian as Chapter 2.

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.

Per @Coffee's recommendation, I would recommend the text Machine Learning: A Bayesian and Optimization Perspective by Sergios Theodoridis along with Pattern Recognition by the same author.

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.

It's not particularly wide in scope and contains some probably unnecessary summaries of linear algebra (insightful nonetheless) and probability (very basic), but the portions on data are nice introductions.

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I hope that is useful to you. I am sure this is just a very personal thing, but I know my wife is the same. She is studying at Uni and the first thing she does is print out the PDFs to study them on paper.

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!)

The two families in this photo are my dear friends from Iraq and Egypt who were pivotal in helping me learn Arabic about 12 years ago. I just caught up with them 2 days ago after being away for the last few years travelling.

My first teacher was from Palestine and I learned a lot from him before I met any Egyptians. He used to take me along to an Arabic-speaking church run by the Iraqi family above so that I could get plenty of exposure to the different dialects.

Note: In articles like this one I usually use affiliate links which means that if you do end up purchasing a book, a very tiny percentage of the cost goes toward helping maintain and improve this site.

Modern Iraqi Arabic does take a fairly strong grammar approach which might suit some people (for me personally I prefer to focus on the dialogues in the book which are also excellent).

I had a similar experience learning Arabic (well maybe not almost marrying an Egyptian), but found that studying so many dialects in such a short time frame was dizzying. It was a relief to finally settle down in Egypt for a couple years and really get my Egyptian sorted and push the shami to the back of my brain...

This is such a great list! I think too few people know of Levantine Arabic resources that are available (at know I was definitely struggling to find some while I was studying in Jordan) so this post is really timely.

I am new here and to the Alteryx ecosystem but passionately want to learn the Alteryx ecosystem starting with Designer so the gurus here can just some good books on the Alteryx Designer. I usually start learning any SW by reading one or two printed or Kindle books and then following up with articles and blogs and community forums

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