Postgraduate Course: Probabilistic Modelling and Reasoning (INFR11050)
Course Outline
| School |
School of Informatics |
College |
College of Science and Engineering |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 11 (Postgraduate) |
Credits |
10 |
| Home subject area |
Informatics |
Other subject area |
None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/pmr |
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| Course description |
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
| Pre-requisites |
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Co-requisites |
|
| Prohibited Combinations |
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Other requirements |
This course has the following mathematics prerequisites:
1 - Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Univariate Gaussian distribution. Joint and conditional distributions. (At the level taught in MfI 1&4).
2 - Linear algebra: Vectors and matrices: definitions, addition. Matrix multiplication, matrix inversion. Eigenvectors, determinants, quadratic forms. (At the level taught in MfI 2&3).
3 - Calculus: Functions of several variables. Partial differentiation. Multivariate maxima and minima. Integration: need to know definitions, including multivariate integration. (At the level taught in MfI 1&2)
4 - Special functions: Log, exp are fundamental. (At the level taught in MfI 1)
5 - Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, ellipsoid and n-dimensional generalizations. (At level taught in MfI 1&4)
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| Additional Costs |
None |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 1, Available to all students (SV1)
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WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | | | | | 10:00 - 10:50 | | Central | Lecture | | 1-11 | | 10:00 - 10:50 | | | |
| First Class |
Week 1, Tuesday, 10:00 - 10:50, Zone: Other. Room 2.13, Old Infirmary Building - High School Yards |
|
| Delivery period: 2010/11 Semester 1, Part-year visiting students only (VV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | | 10:00 - 10:50 | | | | | Central | Lecture | | 1-11 | | | | | 10:00 - 10:50 |
| First Class |
Week 1, Tuesday, 10:00 - 10:50, Zone: Other. Room 2.13, Old Infirmary Building - High School Yards |
Summary of Intended Learning Outcomes
1 - Define the joint distribution implied by directed and undirected probabilistic graphical models.
2 - Carry out inference ingraphical models from first principles by hand, and by using the junction tree algorithm.
3 - Demonstrate understanding of maximum likelihood and Bayesian methods for parameter estimation by hand derivation of estimation equations for specific problems.
4 - Critically discuss differences between various latent variable models for data.
5 - Derive EM updates for various latent variable models (e.g. mixture models).
6 - Define entropy, joint entropy, conditional entropy, mutual information, expected code length.
7 - Demonstrate ability to design, assess and evaluate belief network models.
8 - Use belief network packages (e.g. JavaBayes) and matlab code for probabilistic graphical models.
9 - Demonstrate ability to conduct experimental investigations and draw conclusions from them. |
Assessment Information
Written Examination 70
Assessed Assignments 30
Oral Presentations 0
Assessment
Two assignments, first on building belief networks, second on learning probabilistic models of data.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year. |
| Please see Visiting Student Prospectus website for Visiting Student Assessment information |
Special Arrangements
| Not entered |
Contacts
| Course organiser |
Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk |
Course secretary |
Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk |
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copyright 2010 The University of Edinburgh -
1 September 2010 6:11 am
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