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Nowadays, many books cover the elementary mathematics in machine learning. After I learnt these elementary topics, any good suggestions for computational learni

Nowadays, many books cover the elementary mathematics in machine learning. After I learnt these elementary topics, any good suggestions for computational learning theory?

stochastic_monk 9y ago
I recommend Shai Shalev-Schwartz and Shai Ben-David's Understanding Machine Learning: From Theory to Algorithms [0]. I've also used and found Mohri's Foundations of Machine Learning quite insightful [1]. Usually, between the two books, at least one's proof is easy to follow. Get both unless you're only getting one, in which case, get Shai Shalev-Schwartz's. [0] http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/ http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning... [1] https://mitpress.mit.edu/books/foundations-machine-learning https://mitpress.mit.edu/books/foundations-machine-learning
make3 9y ago
[Deleted]
- stochastic_monk 9y ago
  He's specifically asking about learning theory which is a subfield of machine learning, along the lines of work you'll see at COLT, using concentration bounds, VC theory, and Rademacher complexity. PRML, Murphy, ESL, the Deep Learning book, and the RL introduction are more like what you'd see at ICML or ICLR.
  - make3 9y ago
    thanks, my bad.
- foo101 9y ago
  Why delete the comment? Useful context for stochastic_monk's reply is missing now. You could always preserve the existing and possibly incorrect comment and append an "Update" or "Edit" section to the comment to override your earlier comment.
  - stochastic_monk 8y ago
    Essentially, he asked if the above poster had read Elements of Statistical Learning, Murphy's ML textbook, Bishop's PRML, Reinforcement Learning: An Introduction, and Ian Goodfellow's Deep Learning textbook. I simply clarified that the question was about computational learning theory, a subfield largely started by Leslie Valiant in the form of PAC (Probably Approximately Correct) learning. The difference in emphasis between the machine learning conferences I mentioned helps point out how practical machine learning (like ICML, matching PRML/ML/ESL) and feature extraction/representation learning (like ICLR, perhaps matching portions of both ICML and ICLR), while important, are not what the previous poster was asking about.

Nowadays, many books cover the elementary mathematics in machine learning. After I learnt these elementary topics, any good suggestions for computational learning theory?

stochastic_monk 9y ago

I recommend Shai Shalev-Schwartz and Shai Ben-David's Understanding Machine Learning: From Theory to Algorithms [0]. I've also used and found Mohri's Foundations of Machine Learning quite insightful [1]. Usually, between the two books, at least one's proof is easy to follow. Get both unless you're only getting one, in which case, get Shai Shalev-Schwartz's. [0] http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/ http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning... [1] https://mitpress.mit.edu/books/foundations-machine-learning https://mitpress.mit.edu/books/foundations-machine-learning

make3 9y ago

[Deleted]

stochastic_monk 9y ago
He's specifically asking about learning theory which is a subfield of machine learning, along the lines of work you'll see at COLT, using concentration bounds, VC theory, and Rademacher complexity. PRML, Murphy, ESL, the Deep Learning book, and the RL introduction are more like what you'd see at ICML or ICLR.
- make3 9y ago
  thanks, my bad.
foo101 9y ago
Why delete the comment? Useful context for stochastic_monk's reply is missing now. You could always preserve the existing and possibly incorrect comment and append an "Update" or "Edit" section to the comment to override your earlier comment.
- stochastic_monk 8y ago
  Essentially, he asked if the above poster had read Elements of Statistical Learning, Murphy's ML textbook, Bishop's PRML, Reinforcement Learning: An Introduction, and Ian Goodfellow's Deep Learning textbook. I simply clarified that the question was about computational learning theory, a subfield largely started by Leslie Valiant in the form of PAC (Probably Approximately Correct) learning. The difference in emphasis between the machine learning conferences I mentioned helps point out how practical machine learning (like ICML, matching PRML/ML/ESL) and feature extraction/representation learning (like ICLR, perhaps matching portions of both ICML and ICLR), while important, are not what the previous poster was asking about.