[HN Gopher] Probabilistic Machine Learning: Advanced Topics
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       Probabilistic Machine Learning: Advanced Topics
        
       Author : abhi9u
       Score  : 71 points
       Date   : 2023-08-15 18:57 UTC (4 hours ago)
        
 (HTM) web link (probml.github.io)
 (TXT) w3m dump (probml.github.io)
        
       | cubefox wrote:
       | Judging from the table of contents, he probably could have
       | dropped the "probabilistic" from the title and just called it
       | "machine learning".
        
         | [deleted]
        
       | dang wrote:
       | Related:
       | 
       |  _Probabilistic Machine Learning: Advanced Topics_ -
       | https://news.ycombinator.com/item?id=30552869 - March 2022 (43
       | comments)
        
       | gardenfelder wrote:
       | The introduction book and link to code is at "book1.html"
        
         | abhi9u wrote:
         | Yeah. Kevin Murphy just announced on Twitter that this is the
         | final official version of the "Advanced Topics" book.
        
       | ayhanfuat wrote:
       | Both 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 wrote:
         | what are some good book for people that want to start master in
         | ML?
        
           | runningmike wrote:
           | an opinionated list of great machine learning learning
           | resources:
           | https://nocomplexity.com/documents/fossml/mlcourses.html
        
           | gexaha wrote:
           | I think "Understanding Deep Learning" is very nice -
           | 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)
        
           | ayhanfuat wrote:
           | With 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
           | Francois Chollet's Deep Learning with Python is one of the
           | most accessible books.
           | 
           | [1] https://www.microsoft.com/en-
           | us/research/uploads/prod/2006/0...
        
         | mhh__ wrote:
         | Ironically 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.
        
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       (page generated 2023-08-15 23:00 UTC)