|Module Name||Optimisation Algorithms for Data Analysis|
|ECTS Weighting||5 ECTS|
|Semester taught||Semester 2|
|Module Coordinator/s||Salaheddin Allakkari|
Module Learning Outcomes
On successful completion of this module, students will be able to:
|LO1. Identify optimisation problems and their applications in data analysis and machine learning.|
LO2. Design practical algorithms for solving optimisation problems.
LO3. Compare between different algorithms in terms of complexity and scalability.
The aims of this module are to give the student skills to model, analyse and solve optimisation problems that arise in data analytics.
1. Convex optimization, convexity, duality.
2. Gradient-based methods for solving optimization problems.
3. Linear programming.
4. Data analytics algorithms and applications.
Teaching and learning Methods
Lectures and tutorials
|Assessment Component||Brief Description||Learning Outcomes Addressed||% of total||Week set||Week Due|
Analysis of main assignment repeated and re-written. Worth 100% in reassessment
Contact Hours and Indicative Student Workload
|Contact Hours (scheduled hours per student over full module), broken down by:||22 hours|
|Independent study (outside scheduled contact hours), broken down by:||72 hours|
|Preparation for classes and review of material (including preparation for examination, if applicable||36 hours|
|completion of assessments (including examination, if applicable)||36 hours|
|Total Hours||94 hours|
Recommended Reading List
1. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004, ISBN: 9780521833783;
2. D. P. Bertsekas, J. N. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, Athena Scientific, 2015, ISBN: 1-886529-15-9;
3. D. Bertsimas, R. Weismantel, Optimization over Integers, Dynamic Ideas, 2005, ISBN: 0975914626;
4. J. Leskovec, A. Rajaraman, J. D. Ullman, Mining of Massive Datasets, Cambridge University Press, 2014, ISBN: 9781107077232.
Other/alternative non-module prerequisites: it is recommended that students
have familiarity with basic concepts in linear algebra, probability, and multivariate