Department of Computing
Course 493: Intelligent Data Analysis and Probabilistic Inference

Notices:

Course Outline

Lecture 1 (notes:slides) Bayes' Theorem and Bayesian Inference
Lecture 2 (notes:slides) Bayesian Decision Trees
Lecture 3 (notes:slides) Evidence and message passing
Lecture 4 (notes:slides) Inference in singly connected networks
Lecture 5 (notes:slides) Building networks from data
Lecture 6 (notes:slides) Cause and Independence
Lecture 7 (notes:slides) Model Accuracy
Lecture 8 (notes:slides) Approximate Inference
Lecture 9 (notes:slides) Exact Inference
Lecture 10 (notes:slides) Probability propagation in Join Trees
Lecture 11 (slides) Graphical Models

Coursework: Data Files for the coursework

Errata

Past Exam Papers:

Note that for the topics covered in lectures 11-17 the exam questions may be different in style from earlier years.

2005 , 2006 , 2007 , 2008 , 2009 , 2010
2011 , 2012 , 2013 , 2014 , 2015 , 2016
Tutorial 1 (problems :solutions ) Lamda and Pi messages
Tutorial 2 (problems :solutions) Causal Networks & Multiple Parents
Tutorial 3 (problems : solutions) Multiply connected networks
Tutorial 4 (problems :solutions) Model Accuracy
Tutorial 5 (problems :solutions) Join Trees


Useful Links

Notes on Linear Algebra
Notes from the Mathematics for Inference and Machine Learning Course
Bishop: Chapter on Graphical Models