I took the author of this book's course during my undergrad and quite enjoyed it. It's a good theoretical introduction to machine learning principles. The video lectures are available here: <a href="https://youtu.be/b5NlRg8SjZg" rel="nofollow">https://youtu.be/b5NlRg8SjZg</a><p>As others have mentioned this is a fairly theoretical take on machine learning which may not be useful if you just want to use a deep learning library. That said, I think there is a lot of value in having a deeper theoretical grasp of a topic even when practicing.
This is one of the standard PhD-level textbooks. It's probably a little bit much if you just want to use somebody else's library, but if you're developing your own algorithms or you want to engage seriously with the research literature, you have to know what's in here.<p>The main other books I'd consider are Foundations of Machine Learning (Rostamizadeh, Talwalkar & Mohri) and Machine Learning: A Probabilistic Perspective (Murphy). There are a few others, but I think you'd need a good reason not to pick one of those three.
Looks coherent, informative and not too indulged with proving things with mathematical formulas. Problem is that the only way I could study it with the meticulousness it requires would be during a course. Studying it at my own leisure is a bit too much of a burden.<p>However what irks me a little, as it did with my university's introductory machine learning course, is the direct jump to the algorithms while omitting explaining the basis for the underlying cause why it is the right thing to do. Perhaps it's presumed that the reader knows the basic statistics behind the idea that we can make assumptions of the data and predict values due to central limit theorem. Machine learning itself then can be seen as a derivate of that as more free-form, heuristic approach to cases where the central limit theorem can't be applied or is too restrictive.<p>Am I speaking complete nonsense here? Please those wiser than me, tell me am I right?
I found this book very useful, personally. It does a great job presenting some of the theoretical foundations in an understandable way (e.g. the PAC-learning framework and VC dimension.) I also particularly liked the explanation on PCA.
This seems like a great resource for understanding how machine learning algorithms actually work. Anyone reading the book to help them build more accurate models, might also be interested in supplementing it with more research into the importance of feature engineering before training machine learning models.<p>While feature engineering hasn't been a rigorously studied within the academic literature, this book does have a section on feature generation, which gives some practical tips once your data is in feature matrix form.<p>For those interested in even more, I work on a python library for automated feature engineering called, Featuretools (<a href="https://github.com/featuretools/featuretools/" rel="nofollow">https://github.com/featuretools/featuretools/</a>). It can help when your raw data is still too granular for modeling or comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: <a href="https://www.featuretools.com/demos" rel="nofollow">https://www.featuretools.com/demos</a>.
direct to pdf, <a href="http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf" rel="nofollow">http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning...</a>