Postgraduate Course: Probabilistic Modelling and Reasoning (UG) (INFR11235)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Year 4 Undergraduate)
||Availability||Available to all students
|Summary||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 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)
|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.
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
|High Demand Course?
Course Delivery Information
|Academic year 2022/23, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 30,
Seminar/Tutorial Hours 10,
Feedback/Feedforward Hours 2,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Feedback will primarily be provided through tutorials, an online forum, and direct interactions with the tutors, TA and lecturer.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||Probabilistic Modelling and Reasoning (UG) (INFR11235)||2:00|
On completion of this course, the student will be able to:
- 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
- demonstrate understanding of frequentist 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 and derive EM updates for various latent variable models. Demonstrate ability to implement approximate inference and learning methods
- explain when and why the methods taught in the course are applicable and demonstrate experience gained from practically implementing them
|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
|Keywords||Bayesian statistics,unsupervised learning,probabilistic models
|Course organiser||Dr Michael Urs Gutmann
Tel: (0131 6)50 5190
|Course secretary||Ms Lindsay Seal
Tel: (0131 6)50 2701