View Proposal

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Proposer

Hani Ragab

Title

Machine Learning API

Goal

Design and build a machine learning API for sparse matrices

Description

Several machine learning algorithms exist and can be used to, e.g. predict the value of an output based on inputs. The input is a matrix. Sparse matrices are matrices whose elements are mostly zeroes. Not taking that fact into account results in sub-optimal manipulation of the matrix and a waste of CPU time, RAM and storage. Our objective is to: - Build a library that implements one or more feature selection mechanisms - Implement one or more machine learning algorithms - (Optionally) parallelise computations. - (Optionally) integrate our library in R. Programming Language: - C, C++ - (Optionally) Assembly - Certainly not Java :) Some Wikipedia Reading: - Sparse matrices: https://en.wikipedia.org/wiki/Sparse_matrix - Feature selection: https://en.wikipedia.org/wiki/Feature_selection - Machine learning: https://en.wikipedia.org/wiki/Machine_learning The project can be taken a group of students where each of them will be working on a particular component of the API.

Resources

Background

C/C++

Url

Difficulty Level

Variable

Ethical Approval

None

Number Of Students

5

Supervisor

Hani Ragab

Keywords

Degrees