Probabilistic Modelling and Reasoning (VS1) (P01125)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-PMR-V

When dealing with real world data, we often need to deal with uncertainty. For example, short segments of a speech signal are ambiguous, and we need to take into account context in order to make sense of an utterance. Probability theory provides a rigorous method for representing and reasoning with uncertain knowledge. The course covers two main areas (i) the process of inference in probabilistic reasoning systems and (ii) learning probabilistic models from data. Its aim is to provide a firm grounding in probabilistic modelling and reasoning, and to give a basis which will allow students to go on to develop their interests in more specific areas, such as data-intensive linguistics, automatic speech recognition, probabilistic expert systems, statistical theories of vision etc

Entry Requirements

? This course is only available to part year visiting students.

? This course is a variant of the following course : P00854

? Pre-requisites : PGs only or with permission of Director of Teaching.

Subject Areas

Home subject area

Artificial Intelligence, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 1 (Blocks 1-2)

? Contact Teaching Time : 3 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
10/01/2006	14:00	15:00	Room G.01, William Robertson Building	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Tuesday	14:00	14:50	Central
Lecture	Friday	14:00	14:50	Central

Summary of Intended Learning Outcomes

After completing this course successfully, students will be able to:
-Define the joint distribution implied by directed and undirected probabilistic graphical models.
-Carry out inference ingraphical models from first principles by hand, and by using the junction tree algorithm.
-Demonstrate understanding of maximum likelihood and Bayesian methods for parameter estimation by hand derivation of estimation equations for specific problems.
-Critically discuss differences between various latent variable models for data.
-Derive EM updates for various latent variable models (e.g. mixture models).
-Define entropy, joint entropy, conditional entropy, mutual information, expected code length.
-Demonstrate ability to design, assess and evaluate belief network models.
-Use belief network packages (e.g. JavaBayes) and matlab code for probabilistic graphical models.
-Demonstrate ability to conduct experimental investigations and draw conclusions from them.