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Module Descriptor School of Computer Science and Statistics

Module CodeCS4405
Module NameOptimisation Algorithms for Data Analysis
Module Short Title
Semester TaughtHT (2nd Semester)
Contact Hours
Module PersonnelAssistant Professor Georgios Iosifidis
Learning Outcomes

Students who complete this module should be able to:

1. Understand the principles of convex and non-convex optimization;

2. Model and analyse problems that arise in data analytics;

3. Design algorithms for optimizing data analytic applications.

Learning Aims

The aims of this module are to give the student skills to model, analyse and solve optimisation problems that arise in data analytics.

Module Content

1. Convex optimization, convexity, duality, sub-gradient methods.  

2. Co-ordinate descent methods, parallel and asynchronous optimization algorithms.

3. Integer programming and approximation algorithms.

4. Data analytics algorithms and applications.

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 Prerequisites

It is recommended that students have familiarity with basic concepts in linear algebra, probability, and multivariate calculus.

Assessment Details

Coursework: 30%

Exam: 70%

The coursework is mid-term exams.

Assessment in the Supplemental session will be based on 100% exam.

Module Website
Academic Year of Data2017/18