Postgraduate Course: Probabilistic Modelling and Reasoning (INFR11050)
|School||School of Informatics
||College||College of Science and Engineering
||Availability||Available to all students
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Home subject area||Informatics
||Other subject area||None
||Taught in Gaelic?||No
|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 (not applicable to Visiting Students)
||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)
|Additional Costs|| None
Information for Visiting Students
|Displayed in Visiting Students Prospectus?||Yes
Course Delivery Information
|Delivery period: 2012/13 Semester 1, Available to all students (SV1)
||Learn enabled: No
|Central||Lecture||1-11|| 10:00 - 10:50|
|Central||Lecture||1-11|| 10:00 - 10:50|
||Week 1, Tuesday, 10:00 - 10:50, Zone: Central. LT2 Appleton Tower |
|Main Exam Diet S1 (December)||2:00|
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 matlab code implementing probabilistic graphic models.
9 - Demonstrate ability to conduct experimental investigations and draw conclusions from them.
|Written Examination 80|
Assessed Assignments 20
Oral Presentations 0
One assignment, mainly focussing 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.
o events, discrete variables
o joint, conditional probability
* Discrete belief networks, inference
* Continuous distributions, graphical Gaussian models
* Learning: Maximum Likelihood parameter estimation
* Decision theory
* Hidden variable models
o mixture models and the EM algorithm
o factor analysis
o ICA, non-linear factor analysis
* Dynamic hidden variable models
o Hidden Markov models
o Kalman filters (and extensions)
* Undirected graphical models
o Markov Random Fields
o Boltzmann machines
* Information theory
o entropy, mutual information
o source coding, Kullback-Leibler divergence
* Bayesian methods for
o Inference on parameters
o Model comparison
Relevant QAA Computing Curriculum Sections: Artificial Intelligence
||* The course text is "Pattern Recognition and Machine Learning" by C. M. Bishop (Springer, 2006).
* In addition, David MacKay's book "Information Theory, Inference and Learning Algorithms" (CUP, 2003) is highly recommended.
Timetabled Laboratories 0
Non-timetabled assessed assignments 20
Private Study/Other 53
|Course organiser||Dr Iain Murray
Tel: (0131 6)51 9078
|Course secretary||Miss Kate Weston
Tel: (0131 6)50 2701
© Copyright 2012 The University of Edinburgh - 14 January 2013 4:10 am