Reading strategy for the following books?

I am trying to read the following list of books on statistical learning. I have a BSCS and about 4 yrs of experience working in image processing and parallel programming. I won’t be an expert in the field by any means, however my aim is: not to be a script kiddie, using tools and algorithms without understanding the hows and whys. be able to read and digest the latest research in statistical learning, specially w.r.t computer vision. Prerequisites I have studied in preparation: Matrix Algebra by James E. Gentle Statistical and Mathematical Methods lectures by Carlos Fernandez-Granda Books to read: An Introduction to Statistical Learning with Applications in R by Robert Tibshirani et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Robert Tibshirani et al. Understanding Machine Learning From Theory To Algorithms by Shai Shalev-Shwartz et al. Pattern Recognition and Machine Learning by Christopher M. Bishops Information Theory, Inference, and Learning Algorithms by David J.C. MacKay Deep Learning by Ian Goodfellow et al. Convex Optimization by Stephen Boyd et al. I am looking for a reading strategy. I’d specially appreciate input from users who’ve read the majority of the books. submitted by /u/zindarod [link] [comments]

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Nevin Manimala

Nevin Manimala is interested in blogging and finding new blogs

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