4 ms·

Do what the author did: take Koller's course and follow the research that interests you. In this case, the propagation through factor graphs is also a good anal

by apathy 10y ago

Do what the author did: take Koller's course and follow the research that interests you. In this case, the propagation through factor graphs is also a good analogy for when you start looking at back propagation in NNs and the autoencoder.

blahi 10y ago
This is a very fragmented approach and is very frustrating to go down that road. Every field is filled with hype and people pushing their own agenda and publicity to further their careers. Speaking from experience with self learning discriminatve modeling, the overwhelming majority of the books is either superficial or needlessly complicated. In both of those situations the books are badly written as well. Finding a book that hit the sweet spot for regressions wasn't easy but was doable. I was hoping there would be something similar with Bayesian Nets/Generative Models.
- Florin_Andrei 10y ago
  > Finding a book that hit the sweet spot for regressions wasn't easy but was doable. Could you provide an example?
  - blahi 10y ago
    Regression Modeling Strategies. You need at least some notion of what regressions and probability are all about, but if you have the basics covered, this book will take you through 80% of the journey and the rest is some googling to figure out some concepts that might be murky. This is a book that emphasizes practical applications without getting bent on the math details too much. If on the other hand you are a math whiz, Elements of Statistical Learning is THE book but it expects you to be very proficient in math. Both books are seriously underrated, which is kind of funny to say because you will find only praises about them, but they deserve even more.
    - apathy 10y ago
      I disagree re: ESL and RMS, but Harrell's book is superb. It's really more aimed at biostatisticians and clinicians, though. If the math in ESL gives you trouble, you might prefer http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.p... (and I'm not just saying this because the first author was one of my advisers, although I do think that he and Daniela are particularly gifted teachers). If the math in ESL is too trivial for you, there's always https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS_corrected_1.4.16.pdf https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS... , which covers some graphical modeling strategies in later chapters and even kicks the tires of the autoencoder (imho perhaps the greatest recent advance in neural networks for practitioners) along the way. Koller's course and Ng's course are also good. Ultimately I feel like you have to get the math right or you'll never acquire the intuition that helps you design your own approaches. But you also have to put in the work. That reminds me, tibshirani's Stanford course (accompanies ISL and ESL) is terrific. Better than those other two, actually. I wish Harrell would offer one.
      - blahi 10y ago
        You disagree wit what regarding ESL and RMS? >Ultimately I feel like you have to get the math right or you'll never acquire the intuition that helps you design your own approaches. But what do you mean by that? Do I really need it if my applications are not as demanding as Netflix? I feel like many people consider anything less than phd-level understanding lol worthy, which is simply not true. Majority of analysts out there are doing just fine with canned procedures. Are there something like canned procedures for Bayesian Nets?
        apathy 10y ago
        I disagree that RMS is necessarily better than ESL in some meaningful way. The two are complementary. It's like saying a gravel truck is better than a motorcycle: it all depends on what you want to do with it. Re: "do I really need it?": hell if I know, I'm not you. But my assertion was specifically that if you want to design your own methods (i.e. do research) you need to understand what they are doing. This doesn't seem like a controversial position; an expert is simply a master of the fundamentals.
Florin_Andrei 10y ago
What are the prerequisites for Koller's course? If you mastered the topics in Andrew Ng's course, would that be enough?
- apathy 10y ago
  Possibly, but you'll have to work, hard, to keep up.

by apathy 10y ago

blahi 10y ago

This is a very fragmented approach and is very frustrating to go down that road. Every field is filled with hype and people pushing their own agenda and publicity to further their careers. Speaking from experience with self learning discriminatve modeling, the overwhelming majority of the books is either superficial or needlessly complicated. In both of those situations the books are badly written as well. Finding a book that hit the sweet spot for regressions wasn't easy but was doable. I was hoping there would be something similar with Bayesian Nets/Generative Models.

Florin_Andrei 10y ago
> Finding a book that hit the sweet spot for regressions wasn't easy but was doable. Could you provide an example?
- blahi 10y ago
  Regression Modeling Strategies. You need at least some notion of what regressions and probability are all about, but if you have the basics covered, this book will take you through 80% of the journey and the rest is some googling to figure out some concepts that might be murky. This is a book that emphasizes practical applications without getting bent on the math details too much. If on the other hand you are a math whiz, Elements of Statistical Learning is THE book but it expects you to be very proficient in math. Both books are seriously underrated, which is kind of funny to say because you will find only praises about them, but they deserve even more.
  - apathy 10y ago
    I disagree re: ESL and RMS, but Harrell's book is superb. It's really more aimed at biostatisticians and clinicians, though. If the math in ESL gives you trouble, you might prefer http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.p... (and I'm not just saying this because the first author was one of my advisers, although I do think that he and Daniela are particularly gifted teachers). If the math in ESL is too trivial for you, there's always https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS_corrected_1.4.16.pdf https://web.stanford.edu/~hastie/StatLearnSparsity_files/SLS... , which covers some graphical modeling strategies in later chapters and even kicks the tires of the autoencoder (imho perhaps the greatest recent advance in neural networks for practitioners) along the way. Koller's course and Ng's course are also good. Ultimately I feel like you have to get the math right or you'll never acquire the intuition that helps you design your own approaches. But you also have to put in the work. That reminds me, tibshirani's Stanford course (accompanies ISL and ESL) is terrific. Better than those other two, actually. I wish Harrell would offer one.
    - blahi 10y ago
      You disagree wit what regarding ESL and RMS? >Ultimately I feel like you have to get the math right or you'll never acquire the intuition that helps you design your own approaches. But what do you mean by that? Do I really need it if my applications are not as demanding as Netflix? I feel like many people consider anything less than phd-level understanding lol worthy, which is simply not true. Majority of analysts out there are doing just fine with canned procedures. Are there something like canned procedures for Bayesian Nets?
      - apathy 10y ago
        I disagree that RMS is necessarily better than ESL in some meaningful way. The two are complementary. It's like saying a gravel truck is better than a motorcycle: it all depends on what you want to do with it. Re: "do I really need it?": hell if I know, I'm not you. But my assertion was specifically that if you want to design your own methods (i.e. do research) you need to understand what they are doing. This doesn't seem like a controversial position; an expert is simply a master of the fundamentals.

Florin_Andrei 10y ago

What are the prerequisites for Koller's course? If you mastered the topics in Andrew Ng's course, would that be enough?

apathy 10y ago
Possibly, but you'll have to work, hard, to keep up.