Probabilistic Machine Learning
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Stats for nerds
27recommend
40mentions
35people
2020–2026years
80% recommend20% neutral
Mentions per year
What Hacker News says
- Books? No, not really. Maybe others will have better suggestions for newcomers, sorry. Are you talking research novelty or just applying current methods to a given task?
The latter is covered well by Andrej Karpathy's videos and by just playing around with current models and other tutorials in a small test environment. You don't need to know very much, there's a lot of low-hanging fruit.
For the former, the field is moving rapidly and most of the innovations are coming from papers. Any book that claims to cover deep learning is almost inevitably outdated. Find a university or institution near you and see if they have an undergraduate reading group on deep learning that is open to the public to attend. Mine does, and it's really helpful for staying up to date with the latest ideas. "Probabilistic Machine Learning" by Murphy contains the material that I would consider prerequisite if you want to understand the ideas which underpin modern deep learning (even if it contains virtually no deep learning in it), and I would hope that any student or colleague of mine would be familiar with most of it. But I'm not sure it's good to learn from, and picking all that up takes a while to be honest.
- This is a very strange article that has a “write only” feel to it. I don’t know who the audience for it is, but as someone who actually wants to learn the mathematics of machine learning starting with probability, my plan is to work through Kevin Murphy’s “Probabilistic Machine Learning: An Introduction” https://probml.github.io/pml-book/book1.html which looks excellent.
- This post resonates with me. As a book-lover and collector, I prefer my books to be of high quality, long lasting, and beautiful to hold and look at. If I'm spending $ or $$$ on books, I want to get a good reading experience, and that often starts with both the quality of paper as well as the print.
Like the OP himself (hi Alex), I'm also frustrated with the quality of books (many of which are print-on-demand) from Amazon.
Case in point: Algorithms Illuminated by Tim Roughgarden (https://algorithmsilluminated.org/). Great set of books, but poor quality, sadly. All printed on demand.
Ditto with Sebastian Raschka's (early) books from Packt Publishing (Python Machine Learning). Great content, but poor printing (very low contrast, etc).
This is not a debate on ebooks vs paper books, or PDF vs something else. It's about quality of printed books.
I paid $$$ for Kevin Murphy's _Probabilistic Machine Learning_ from MIT Press. Excellent quality paper, great printing. It's a keeper. I'll be reading it years from today. Just as I read (and have) books from decades ago. I might pass 'em on to somebody well deserving, not junking them to recycle.
Like OP, I want my books and my library to be long lasting. It's a joy. And I'm happy to pay a higher price if needed.
- Then you may enjoy Elements of Statistical Learning[1] or Kevin Murphy's books[2][3][4], which this chapter is heavily indebted to; it is mostly a short gloss of the topics covered in those books.
[1]: https://hastie.su.domains/ElemStatLearn/
[2]: https://probml.github.io/pml-book/book0.html
- My go to for teaching statistics is Statistical Rethinking. It’s basically a course in how to actually thing about modeling: what you’re really looking for is analyzing a hypothesis, and a model may be consistent with a number of hypotheses, figuring out what hypotheses any given model implies is the hard/fun part, and this book teaches you that. The only drawback is that it’s not free. (Although there are excellent lectures by the author available for free on YouTube. These are worth watching even if you don’t get the book.)
I also recommend Gelman’s (one of the authors of the linked book) Regression and Other Stories as a more approachable text for this content.
Think Bayes and Bayesian Methods for Hackers are introductory books from a beginner coming from a programming background.
If you want something more from the ML world that heavily emphasizes the benefits of probabilistic (Bayesian) methods, I highly recommend Kevin Murphy’s Probabilistic Machine Learning. I have only read the first edition before he split it into two volumes and expanded it, but I’ve only heard good things about the new volumes too.
- Kevin Murphy's book, particularly volume 2, is really well organized and summarizes the state of the art: https://probml.github.io/pml-book/book2.html
It's a really good entry point to current ML, written by an expert who has been in the field of generative models before they incorporated deep features.
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