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 : Mathematics for Informatics 3 and Mathematics for Informatics 4 or equivalent. For Informatics PG students only, or by special permission of the School. The background needed to successfully take this course is a good grounding in mathematics, particularly with regard to probability and statistics, vectors and matrices. The mathematical level required is similar to that which would be obtained by students who did not have significant difficulties with the courses Mathematics for Informatics 1-4 taken in the first two years of the Informatics undergraduate syllabus. Also, a reasonable level of familiarity with computational concepts is assumed.

Subject Areas

Home subject area

Bioinformatics, (School of Informatics, Schedule O)

Other subject areas

Database Systems, (School of Informatics, Schedule O)

Learning from Data, (School of Informatics, Schedule O)

Computational Systems Biology, (School of Informatics, Schedule O)

Intelligent Robotics, (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
21/09/2006	14:00	14:50	Room G.04, William Robertson Building	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	14:00	14:50	Central
Lecture	Thursday	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.