Department
of
Computing
Course 493: Intelligent Data Analysis and Probabilistic Inference
Notices:
A revision lecture will be
arranged early in the summer term. The details will be posted here in
due course.
- Lecture 1 (notes:slides): Bayes'
Theorem and Simple
Bayesian Inference
- Lecture 2 (notes:slides):
Bayesian Decision Trees
- Lecture 3 (notes:slides):
Evidence and message passing
- Lecture 4 (notes:slides):
Multiple parents
- Lecture 5 (notes:slides):
Probability propagation in singly connected networks
- Lecture 6 (notes:slides):
Building networks from data
- Lecture 7 (notes:slides): Cause and
Independence
- Lecture 8 (notes:slides):
Model Accuracy
- Lecture 9 (notes:slides):
Approximate Inference
- Lecture 10 (notes:slides): Exact
Inference
- Lecture 11 (notes:slides): Probability
propagation in Join Trees
- Lecture 12 (notes:slides):
Introduction to Graphical Models
- Lecture 13 (notes:slides)
: Sampling and Resampling
- Lecture 14 (notes:slides)
: Data Models and Distributions
- Lecture 15 (notes:slides)
: Feature Reduction, Principal
Component Analysis
- Lecture 16 (notes:slides)
: Linear Discriminant Analysis
- Lecture 17 (notes:slides)
: Small Sample size problems
- Lecture 18 (notes:slides)
: Support Vector Machines
Errata