Textbook:
<br>Pattern Recognition and Machine Learning (Christopher Bishop,  Springer, 2006)
<br>
<br>Syllabus:
<br>Introduction
<br>Linear models for regression
<br>Linear models for classification
<br>Neural networks
<br>Kernel methods
<br>Sparse kernel machines
<br>Mixture models and EM
<br>Approximate inference
<br>Sampling methods
<br>Continuous Latent Variables
<br>Linear Dynamical Systems
<br>Combining Models
<br>
<br>Evaluation:
<br>Homework 40%
<br>Midterm 15%
<br>Final 15%
<br>Term project 30%
<br>
<br>Website: https://sites.google.com/site/nthuslt2011/