Module Descriptor School of Computer Science and Statistics
|Module Name||Optimisation Algorithms for Data Analysis|
|Module Short Title|
|Semester Taught||HT (2nd Semester)|
|Module Personnel||Assistant Professor Georgios Iosifidis|
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.
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, 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.
It is recommended that students have familiarity with basic concepts in linear algebra, probability, and multivariate calculus.
The coursework is mid-term exams.
Assessment in the Supplemental session will be based on 100% exam.
|Academic Year of Data||2017/18|