Data Analysis 2014/15

Background material:

For lec 1-2,4: we covered basic statistics that is explained nicely in many introductory books. I recommend:

An introduction to the Boostrap by Efron and Tibshairani – imho the right approach for statistics in the modern era

The elements of Statistical Learning by Hastie, Tibshirani, Friedman. A different approach from the one we develop in this course, but the intro to linear regression (later multi-linear regression) is effective.

A somewhat epicurean suggestions is to use the classical Numerical Recepies book (e.g., in the C or C++ version) to understand how the concepts we introduce are implement. NRC is usually doing also a fantastic job in explaining the essence of the statistical or computational ideas behind the math.

For lecture 3, and for more background on graphical models use:

Probabilistic Graphical Models by Friedman and Koller.


Problem set for lec 1-2: pdf

Notes for lecture 5 ppt

Notes for lecture 8 ppt

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