Learning Systems Project – CS236757

Course Resources

The purpose of this provide you is to provide you with a list of resources, which will enable a successful completion of the project. The resources are divided into two parts: those dealing with general machine learning techniques and algorithms and those dealing with portfolio selection.

Machine Learning Books and Tutorials :

1. Tom Mitchell, Machine Learning, McGraw Hill, 1997. A general Machine Learning textbook.
2. R.O. Duda, P.E. Hart and D.E. Stork, Pattern Classification (2^nd edition), Wiley Interscience.
3. Christopher J.C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition.
4. The Kernlel Machines Web site. Lots of useful leads and papers on SVMs and Kernel machines.
5. The Boosting Research site. Lots of leads and papers on boosting algorithms.
6. Neural Networks Tutorial with Java Applets.
7. Neural Network: Advanced Tutorial.
8. Rabiner’s Hidden Markov Models (HMMs) Tutorial.
9. Some links related to unsupervised learning and clustering:
1. The Clustering Home
2. Cluster Analysis
10. Genetic programming resources. Genetic algorithms tutorial web site.
11. The StatSoft Home Page. A Statistics electronic textbook.
12. Avrim Blum’s survey article on Online Algorithms in Machine Learning.

Machine Learning Software:

1. LibSVM – A Library for Support Vector Machines.
2. C4.5 – A decision tree code.
3. A great Classification Matlab Toolbox, by Elad Yom-Tov. The toolbox implements all the algorithms in the book “Pattern Classification”
4. Netlab Neural Network Software

Portfolio Selection Resources

1. Malkiel’s Random Walk Down Wall Street Book
2. Tom Cover’s Universal Portfolios paper. Also, an article in Stanford Online Report . And another article
3. Helmbold’s et al. paper on Portfolio Selection Using Multiplicative Updates.
4. Technical Analysis 101 Directory
5. Jon Murphy’s Ten Laws of Technical Trading.
6. A short article on Fundamental Analysis.
7. Fundamental analysis or technical analysis?