Topics:
* Basic techniques/theory for support vector machine (SVM).
* Principal component analysis (PCA); singular value decomposition (SVD); selection of component number.
* Multi-linear PCA; high order SVD; rank (component number) selection.
 Some technical tools/methods useful for complex and high-dimensional statistical analysis.
- Shapiro, A. (1986). Asymptotic theory of over-parameterized structural models. J. Amer. Statist. Assoc.,
81, 142-149.
- Tsiatis, A.A. (2006). The geometry of in
uence functions. Chapter 3 in Semiparametric Theory and
Missing Data. Springer, New York.
 Learn patterns and trends from data with problems in genomics, ltering and control of dynamical system, and portfolio theory
- Hastie, Tibshirani & Friedman (2001, 2009). The Elements of Statistical Learning. Springer.
- Lai & Xing (2008). Statistical Models and Methods for Financial Markets. Springer.
- Lai & Xing (2011). Active Risk Management: Financial Models and Statistical Methods, Chapman &
Hall/CRC.