——– Probability, Statistics, Machine Learning and Optimization —-

Probability Theory and Examples by Rick Durrett: http://services.math.duke.edu/~rtd/PTE/PTE4_1.pdf

– one of the preferred grad level probability textbooks

Jeff Miller has an excellent series of youtube videos

Probability primer: https://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4

– covers some topics in probability (634-635) and stat theory (654-655)

Machine learning: https://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA

– covers many topics in stat theory (654-655) and applied stats (664-665)

– also covers topics in machine learning and bayesian stat courses
Introduction to Statistical Learning (http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf) and Elements of Statistical Learning (https://web.stanford.edu/~hastie/Papers/ESLII.pdf)

– these are great places to turn for your first (and second) foray applied statistics and machine learning

Michael Jordan’s suggested reading list for statistics PhD: https://honglangwang.wordpress.com/2014/12/30/machine-learning-books-suggested-by-michael-i-jordan-from-berkeley/

The deep learning book: http://www.deeplearningbook.org/

– Introductory/intermediate level textbook form some of the masters

– Also a good book to machine learning and optimization
Convex Optimization by Vandenberghe and Boyd: https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf

– the standard introduction to optimization

– also see the course webpage: http://www.seas.ucla.edu/~vandenbe/ee236b/ee236b.html

– and Stephen Boyd’s youtube lectures: https://www.youtube.com/view_play_list?p=3940DD956CDF0622

Optimization Methods for Large-Scale Machine Learning: https://arxiv.org/pdf/1606.04838.pdf

– overview of many of the modern optimization methods that statisticians/machine learning researchers should at least be aware of

———- Computation ———–

R for Data Science http://r4ds.had.co.nz/

– fantastic, free, online textbook introductory to intermediate R

STOR 320: Intro to Data Science https://idc9.github.io/stor390/

– introduces R and data science

Python Data Science Handbook: https://jakevdp.github.io/PythonDataScienceHandbook/

– introduction to doing statistics/machine learning in Python
Duke’s Computational Statistics in Python: https://people.duke.edu/~ccc14/sta-663/ReveiwAndTrends.html

– covers a huge number of topics in computational statistics from advanced python to MCMC to GPU computing

Computational Linear Algebra: https://github.com/fastai/numerical-linear-algebra

– covers things like PCA, robust PCA, non-negative matrix factorization, large scale linear regression all in Python.

Lot’s of small coding examples in Python/R: https://chrisalbon.com/

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