SAI2010: Statistical Methods for Intelligent Information Processing (3 credits)


Instructor: Prof. Shou-de Lin ( , Office 333

Classroom: CSIE 104

Meeting Time: Th 14:20-17:20 pm

Office Hour:  After class or by appointment

TA: Yu-Shi Lin (, En-Shi Yen (

Course Description:

This course teaches how to exploit probabilistic methods for intelligent information processing. We will cover the following topics:
1. Generative Models (Bayesian approaches)
2. Graphical Influence and Learning Models
3. Sampling modes for approximation, and the EM algorithm.

Programming Assignments: (65%)
Final Project: (35%)

Reference books:

    Textbook: Probabilistic Graphical Models Principles and Techniques Daphne Koller and Nir Friedman (ISBN 0-262-01319-3)

Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman (ISBN 0387952845)
Pattern Recognition and Machine Learning by Chris Bishop (SBN 0387310738)
Machine Learning by Tom Mitchell (ISBN 0070428077)
The EM algorithm and related statistical models / edited by Michiko Watanabe, Kazunori Yamaguchi
Reinforcement learning : an introduction / Richard S. Sutton and Andrew G. Barto MIT Press, c1998