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ELEC70037 Topics in Large Dimensional Data Processing

Lecturer(s): Dr Wei Dai; Dr Stefan Vlaski


In this module, you will acquire skills to design algorithms to process and to analyse large dimensional data. You will be exposed to various application domains where the development of tools for analysis of large dimensional data is essential. This includes applications in image/video processing, machine learning, collaborative filtering, social network, financial data analysis.

Learning Outcomes

Upon successful completion of this module, you will be able to: 1. Design algorithms for large dimensional data processing, with the focus on finding sparse structures hidden in the data for dimension reduction; 2. Use linear algebra tools to model problems involving large dimensional data 3 Develop optimization and statistics tools to solve problems involving large dimensional data. 4. Evaluate advantages and disadvantages of different algorithms for analysis of large dimensional data in real-world applications, including for example imaging inverse problems, online recommendation, machine learning.


1. Introduction to large dimensional data processing: the key challenge. 2. Mathematical tools for large dimensional data processing ** Matrix analysis ** Optimization: Convexity, duality, and algorithms 3. Formulations to explore sparsity in the data ** Linear inverse problems ** Sparse linear inverse problems ** Sparse bilinear inverse problems 4. Selected real-world applications ** Google page rank, compressed sensing, denoising, robust classification, Netflix problem, blind deconvolution, super-resolution, etc.
Exam Duration: N/A
Exam contribution: 0%
Coursework contribution: 100%

Term: Autumn

Closed or Open Book (end of year exam): N/A

Coursework Requirement:
         Coursework only module

Oral Exam Required (as final assessment): N/A

Prerequisite module(s): None required

Course Homepage: unavailable

Book List:
Please see Module Reading list