CSU44005 – Optimisation Algorithms for Data Analysis

Module CodeCSU44005
Module Name Optimisation Algorithms for Data Analysis
ECTS Weighting[1]5 ECTS
Semester taughtSemester 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.  

Module Content

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 Details

Assessment ComponentBrief Description Learning Outcomes Addressed% of totalWeek setWeek Due
Final
Examination
Take-Home ExamLO1-LO370%
Mid-term
Exams
Mid-Term AssignmentLO1-LO330%

Reassessment Details

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
Lecture17 hours
Laboratory5 hours
Independent study (outside scheduled contact hours), broken down by:72  hours
Preparation for classes and review of material (including preparation for examination, if applicable36 hours
completion of assessments (including examination, if applicable)36 hours
Total Hours94 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.

Module Pre-requisites

Other/alternative non-module prerequisites: it is recommended that students
have familiarity with basic concepts in linear algebra, probability, and multivariate
calculus.

Module Co-requisites

None

Module Website

Blackboard