ELEC97088 (EE4-66) Topics in Large Dimensional Signal ProcessingLecturer(s): Dr Wei Dai Aims
This module is designed for students to gain skills on modeling and algorithm-design in processing and analyzing large dimensional data, and to expose them to various application domains including image/video processing, machine learning, collaborative filtering, social network, financial data analysis, etc.
Learning Outcomes
Upon successful completion of this module, students will be able to:
1. Present ideas, modelling, theory, and algorithm designs for large dimensinoal data processing, with the focus on finding sparse structures hidden in the data for dimension reduction; 2. Demonstrate that basic tools in linear algebra, optimization and statistics can be developed to solve real-world problems involving large dimensional data. 3. Gain skills to work with real-world applications including computational imaging, online recommendation, machine learning, etc. Syllabus
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 Coursework contribution: 100% Term: Autumn Closed or Open Book (end of year exam): N/A Coursework Requirement: Coursework involves paper study and 6 minute presentation. The detailed format and procedure will be announced in lectures. The paper study is designed to encourage students to go beyond the taught materials and cultivate a good taste about important techniques and applications. The final presentations will help students largely broaden their views of the topic, witness how their peers use their judgement to choose a sub-topic to study, and get exposed to critical thinking of others. Oral Exam Required (as final assessment): N/A Prerequisite module(s): None required Course Homepage: unavailable Book List: Please see Module Reading list
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