My reading list, as well as some highly recommended books and papers with insightful points.

**Books:**

Highlight:

M.I.Jordan, 2003.**An Introduction to Probabilistic Graphical Models.**- More: http://www.cs.princeton.edu/courses/archive/fall11/cos597C/

Reference/reading:

*The element of statistical learning: data mining, inference and prediction.*Trevor Hastie, Robert Tibshirani and Jerome Friedman. Springer, 2009.*Matrix Computations*. Gene H. Golub and Charles F. Van Loan. Johns Hopkins University Press, 1997.*Elements of Information Theory*. Cover, Thomas M and Thomas, Joy A. Wiley-interscience 2006.*All of Statistics: A Concise Course in Statistical Inference*. Wasserman, Larry. Springer, 2003.

Read:

*Introduction to linear algebra*. Strang, G. Wellesley Cambridge Press, 2003.*Scientific Computing: An Introductory Survey*. Michael T. Heath. McGraw-Hill, 2002.*Principles of Mathematical Analysis*. Walter Rudin. McGraw-Hill, 1976.*Applied multivariate statistical analysis.*Johnson, R.A. and Wichern, D.W. Prentice Hall Upper Saddle River, NJ, 2002.*Convex optimization.*Stephen Boyd and Lieven Vandenberghe. Cambridge university press, 2009.- Andrew Ng’s handout for machine learning course in stanford: http://cs229.stanford.edu/schedule.html. Highly recommended to learn these basic however important topics: discriminative learning, generative learning, SVM, and bayesian learning. Well organised and easy to follow. Also there are some reviews on linear algebra, probability and optimization, which can wrap your knowledge for further learning.

*Introduction to Data Mining*. Tan, P.N. and Steinbach, M. and Kumar, V. and others. Pearson Addison Wesley Boston, 2006.*Introduction to Information Retrieval*. Manning, Christopher D. and Raghavan, Prabhakar and Schtze, Hinrich. Cambridge University Press, 2008.*Database System Concepts*. A. Silberschatz, H.F. Korth, S. Sudarshan. McGraw-Hill, 2010.

*Compilers*. Dr. Matt Poole 2002, edited by Mr. Christopher Whyley. Course notes for module CS 218, Department of Computer Science, University of Wales Swansea.*Models of Computation: Automata, Formal Languages and Communicating Processes*. Jos C.M. Baeten, 2011. Course notes for module 2IT15, Department of Mathematics & Computer Science, Eindhoven University of Technology.*Introduction to Algorithms*(3rd edition). T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein. MIT Press, 2009.

**Interesting articles:**

*Statistical Modeling: The Two Cultures.*Leo Breiman.*深度学习：推进人工智能的梦想.*Kai Yu.

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