Postgraduate Course: Probabilistic Machine Learning (INFR11298)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Real world data are often riddled with uncertainty. For example, speech, medical tests, and signals from autonomous-vehicle sensors are often ambiguous, creating uncertainty about what the data mean and how to act. Probabilistic machine learning addresses this by using probability to build models and make predictions, returning distributions rather than single guesses and updating beliefs as new evidence arrives. This course covers the foundations of probabilistic machine learning, giving you the tools to understand methods from first principles and to design your own. The skills you gain provide a solid foundation for further studies in machine learning and its application across domains, including statistical language and speech processing, computer vision, robotics, bioinformatics, or computational neuroscience, amongst many more.
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| Course description |
The course provides you with a thorough understanding of probabilistic machine learning. You will learn to derive core algorithms from first principles and reason about their assumptions. By the end, you will have a strong foundation for understanding extensions, further developments, and applications. The course covers five main topics:
1. probabilistic graphical models
2. exact inference
3. actions and decision making
4. learning
5. approximate inference and learning
The course will be delivered in a series of lectures and exercises, supported by an online discussion forum. It will mostly feature pen-and-paper work and is not an applied course.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | MSc and Undergraduate students are eligible to register on this course
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-requisites | 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. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2026/27, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 30,
Seminar/Tutorial Hours 8,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
156 )
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| Assessment (Further Info) |
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %
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| 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 |
Minutes |
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| Main Exam Diet S1 (December) | Probabilistic Machine Learning (INFR11298) | 120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- define the joint distribution implied by directed and undirected graphical models, and read out statistical independence assumptions from the graphs
- carry out inference in graphical models from first principles by hand
- predict the outcome of actions and decide optimal actions in the face of uncertainty
- derive learning rules for model parameters from first principles, using both frequentist (e.g., maximum likelihood) and Bayesian (e.g., MAP / posterior) methods for specific problems
- identify sources of computational intractability and derive approximate inference and learning algorithms for widely used probabilistic models using variational inference and Monte Carlo methods
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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 |
| Keywords | Machine Learning,Unsupervised Learning,Probabilistic Modelling,Bayesian Statistics |
Contacts
| Course organiser | Dr Michael Gutmann
Tel:
Email: Michael.Gutmann@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 5194
Email: lindsay.seal@ed.ac.uk |
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