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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Probabilistic Modelling and Reasoning (UG) (INFR11235)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course follows the delivery and assessment of Probabilistic Modelling and Reasoning (INFR11134) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11134 instead.
Course description This course follows the delivery and assessment of Probabilistic Modelling and Reasoning (INFR11134) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11134 instead.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Probabilistic Modelling and Reasoning (INFR11134)
Other requirements This course follows the delivery and assessment of Probabilistic Modelling and Reasoning (INFR11134) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11134 instead.

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.

Mathematics prerequisites:
1. Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Joint and conditional distributions. Univariate and multivariate Gaussian distribution
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

Programming prerequisite: A basic level of programming is assumed and not covered in lectures.

There will be some assumption that people are familiar with machine learning concepts.
Information for Visiting Students
Pre-requisitesAs above.
High Demand Course? Yes
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. define the joint distribution implied by directed and undirected probabilistic graphical models, convert between different graphical models, and carry out inference in graphical models from first principles by hand
  2. demonstrate understanding of frequentist and Bayesian methods for parameter estimation by hand derivation of estimation equations for specific problems
  3. critically discuss differences between various latent variable models for data and derive EM updates for various latent variable models. Demonstrate ability to implement approximate inference and learning methods
  4. explain when and why the methods taught in the course are applicable and demonstrate experience gained from practically implementing them
Reading List
None
Additional Information
Graduate Attributes and Skills The student will be able to reason about uncertainty, an important transferable skill.

In addition the student will be able to:
- Undertake critical evaluations of a wide range of numerical and graphical data
- Apply critical analysis, evaluation and synthesis to forefront issues, or issues that are informed by forefront developments in the subject / discipline / sector
- Identify, conceptualise and define new and abstract problems and issues
- Develop original and creative responses to problems and issues
- Critically review, consolidate and extend knowledge, skills, practices and thinking in a subject / discipline / sector
- Deal with complex issues and make informed judgements in situations in the absence of complete or consistent data / information
KeywordsBayesian statistics,unsupervised learning,probabilistic models
Contacts
Course organiserDr Michael Urs Gutmann
Tel:
Email: Michael.Gutmann@ed.ac.uk
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 5194
Email: lindsay.seal@ed.ac.uk
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