Postgraduate Course: Probabilistic Modelling and Reasoning (INFR11050)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||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.
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
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
1 - Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Joint and conditional distributions.
2 - Linear algebra: Vectors and matrices: definitions, addition. Matrix multiplication, matrix inversion. Eigenvectors, determinants, quadratic forms.
3 - Calculus: Functions of several variables. Partial differentiation. Multivariate maxima and minima. Integration: need to know definitions, including multivariate integration.
4 - Special functions: Log, exp are fundamental.
5 - Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, ellipsoid and n-dimensional generalizations.
6 - Graph theory: Basic concepts and definitions: vertices and edges, directed and undirected graphs, trees, paths and cycles, cliques.
Programming prerequisite: A basic level of programming is assumed and not covered in lectures. The assessed assignment will involve some programming, probably in MATLAB.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2015/16, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Seminar/Tutorial Hours 8,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||One assignment, mainly focussing on learning probabilistic models of data.
You should expect to spend approximately 20 hours on the coursework for this course.
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.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||2:00|
| 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.
|* 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.
|Course organiser||Dr Amos Storkey
Tel: (0131 6)51 1208
|Course secretary||Miss Maree Matheson
Tel: (0131 6)50 9989
© Copyright 2015 The University of Edinburgh - 18 January 2016 4:13 am