do you have any recommendations for books on machine learning that I could read?
This is a question I frequently am asked by students looking to transition into machine learning or data science.
My own experience is that it's helpful, with a new field, to read the same
topic described by different people, particularly people coming from different backgrounds who emphasize different aspects.
In 2018, for machine learning, you might start with these:
- Elements of Statistical Learning is written by people coming from applied computational statistics as a background and has the added advantages of being nearly comprehensive, well-known to other students, and free. This is a good "hello world" for machine learning as viewed by applied computational statisticians.
- Chris Bishop's Pattern Recognition and Machine Learning" is almost as well known, at least within the field of generative modeling in machine learning. Bishop's educational background was in theoretical physics, so you might find it appealing if you are coming from that background as well.
- Kevin Murphy's book on machine learning is quite popular. Kevin is a computer scientist by background.
- I really enjoy Foundations of Machine Learning, though it doesn't seem to be as widely-read by students as the above 3 books. In particular I find it much more mathematical, which may just be the kind of book I like, with good appendices relating the "need-to-know" bounds and inequalities on which much of the PAC framework in machine learning is based. Also it's got good sections on reinforcement learning, which is under-discussed in most books on ML.