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Module Descriptor School of Computer Science and Statistics

Module CodeCS7DS2
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.

2. Gradient-based algorithms, parallel and asynchronous algorithms.

3. 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; 

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 Data2018/19