# DEGREE REGULATIONS & PROGRAMMES OF STUDY 2023/2024

### Timetable information in the Course Catalogue may be subject to change.

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# 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 SCQF Credits 20 ECTS Credits 10 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. 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.
 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.
 Pre-requisites As above. High Demand Course? Yes
 Academic year 2023/24, Available to all students (SV1) Quota:  None Course Start Semester 2 Timetable Timetable Learning and Teaching activities (Further Info) Total Hours: 200 ( 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 152 ) Assessment (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 % Additional Information (Assessment) Exam 75% Quizzes 25% Feedback Feedback will primarily be provided through tutorials, an online forum, and direct interactions with the tutors, TA and lecturer. Exam Information Exam Diet Paper Name 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 handdemonstrate understanding of frequentist and Bayesian methods for parameter estimation by hand derivation of estimation equations for specific problemscritically 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 methodsexplain when and why the methods taught in the course are applicable and demonstrate experience gained from practically implementing them
 None
 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: Email: Michael.Gutmann@ed.ac.uk Course secretary Ms Lindsay Seal Tel: (0131 6)50 2701 Email: lindsay.seal@ed.ac.uk
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