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Probabilistic Machine Learning: Advanced Topics
- dang 3y agoRelated: Probabilistic Machine Learning: Advanced Topics - https://news.ycombinator.com/item?id=30552869 https://news.ycombinator.com/item?id=30552869 - March 2022 (43 comments)
- cubefox 3y agoJudging from the table of contents, he probably could have dropped the "probabilistic" from the title and just called it "machine learning".
- deleted 3y ago[deleted]
- shepardrtc 3y agoHis old book did just that: https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020 https://www.amazon.com/Machine-Learning-Probabilistic-Perspe...
- kitanata 3y ago[flagged]
- tayo42 3y ago1200 pages? Do you really read this straight through or just refer to it? That is over whelming to me at least.
- quickthrower2 3y agoStarts off banging though dozens of probability distributions without a pause for breath. Feels like a reference material.
- ac275 3y ago[dead]
- coderatlarge 3y agoI find myself wishing for a book that instead of listing techniques with a few shallow examples per technique , would instead focus on a meaty problem and then apply different techniques to it iteratively showing how a practitioner would derive value. Is anyone aware of such books ?
- mathisfun123 3y agoThat's just a dissertation. If you mean that also it should be written in a pedagogically sound way, I don't think I've ever seen anything like that outside of for historically important problems where a single textbook/class might be about the problem (think special relatively). The reason these kinds of things don't get written is the same reason docs for some random package don't get turned into books either: ain't no one got time for that when they're working on solving more such problems (and if you're not so expert that you're busy then you can't write the book).
- coderatlarge 3y agoFair enough, thanks. One of the reasons I think that might be a better way at the problem is it would encourage readers to really dig into a specific problem and get a sense for behaviors in the data, as they develop their understanding of what each technique can add. Part of the trick would be to get a really good publicly available data set about a problem with enduring significance. Maybe sometime from an old competition that garnered a lot of interest in its day, like say the Netflix recommendation problem. In a way, such a book would walk you through a lot of the stages of learning that the typical book presumes you would anyway do on your own in your own practice. For me, working on my own, it would fill in some of the practical questions of what someone working with colleagues would learn about by osmosis.
- gardenfelder 3y agoThe introduction book and link to code is at "book1.html"
- abhi9u 3y agoYeah. Kevin Murphy just announced on Twitter that this is the final official version of the "Advanced Topics" book.
- ayhanfuat 3y agoBoth the intro and this one are great reference books but I don't find them suitable to study as the main textbook. They cover a large number of topics so the depth of each topic is pretty limited. Keep in mind if you are considering to study these.
- pajep 3y agowhat are some good book for people that want to start master in ML?
- ayhanfuat 3y agoWith a similar probability focus, Pattern Recognition and Machine Learning by Christopher Bishop [1] is pretty good. If you are looking into deep learning specifically, I think François Chollet's Deep Learning with Python is one of the most accessible books. [1] https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf https://www.microsoft.com/en-us/research/uploads/prod/2006/0...
- runningmike 3y agoan opinionated list of great machine learning learning resources: https://nocomplexity.com/documents/fossml/mlcourses.html https://nocomplexity.com/documents/fossml/mlcourses.html
- gexaha 3y agoI think "Understanding Deep Learning" is very nice - https://udlbook.github.io/udlbook/ https://udlbook.github.io/udlbook/ (an covers almost all topics, it has maybe just a couple of omissions, such as Multimodal Learning, NERFs and Time Series Prediction)
- mhh__ 3y agoIronically I found it to be too deep. I want a quick feel for the mathematical structure and ergonomics of a field before really diving into 400 pages on logistic regressions.