Postgraduate Course: Large Scale Optimization for Data Science (MATH11147)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course provides an overview of modern optimization methods and tools for applications in machine learning, artificial intelligence, and data science. A particular emphasis will be on the scalability of algorithms to large-scale problems. The course will cover convexity and its role in optimization, gradient methods, subgradient methods, proximal gradient methods, accelerated and stochastic variants, Frank-Wolfe methods, coordinate descent methods, alternating minimization methods, and alternating direction method of multipliers (ADMM).
The successful applications of these methods in various optimization problems arising from machine learning, artificial intelligence, and data science will be discussed.
The practical component of this course will consist of computing laboratory work using appropriate optimization tools and solvers using a high-level language such as Python. |
| Course description |
A tentative list of course topics is as follows:
- Convexity, gradients, subgradients
- Gradient methods, subgradient methods, and their projected versions
- Accelerated and stochastic variants
- Proximal gradient methods and accelerated versions
- Franke-Wolfe methods
- Coordinate descent and block coordinate descent methods
- Alternating minimization methods
- Alternating direction method of multipliers (ADMM)
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Fundamentals of Optimization (MATH11111)
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2026/27, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 18,
Seminar/Tutorial Hours 5,
Supervised Practical/Workshop/Studio Hours 4,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
69 )
|
| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam 80 %, Coursework 20 %
|
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
|
| Main Exam Diet S2 (April/May) | Large Scale Optimization for Data Science (MATH11147) | 120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Model real-life problems arising from applications in machine learning, artificial intelligence, and data science as optimization problems.
- Choose a solution method appropriate to the characteristics of a given problem.
- Explain how complexity analysis can be used to assess the efficiency of optimization techniques.
- Identify trade-offs between time an accuracy for optimization methods.
- Formulate scalable and accurate implementations of the most important optimization algorithms for applications in machine learning, artificial intelligence and data science.
|
Reading List
1. First-order methods in optimization. Beck, Amir, Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, 2017.
2. Optimization for data analysis. Wright, Stephen J., Recht, Benjamin, Cambridge University Press; 2022. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | ODS,Data Science |
Contacts
| Course organiser | Prof Jacek Gondzio
Tel:
Email: J.Gondzio@ed.ac.uk |
Course secretary | Miss Gemma Aitchison
Tel: (0131 6)50 9268
Email: Gemma.Aitchison@ed.ac.uk |
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