Teaching Responsibility

LJMU Schools involved in Delivery:

Computer Science and Mathematics

Learning Methods

Lecture
Practical

Module Offerings

7124COMP-JAN-CTY

Aims

The aim of this module is to complete the data science “process” by building data products that feedback and influence the real world using the results of that data science process. In doing so, students gain experience in bringing to fruition the data science process.

Learning Outcomes

1.
Design a data application using large scale data storage and machine learning tools
2.
Construct a data application using large scale data storage and machine learning tools
3.
Design a machine learning/analytics exercise for a given subject area
4.
Develop a machine learning/analytics solution for a given subject area

Module Content

Outline Syllabus:Review of large scale data storage platforms and machine learning tools e.g. Hadoop and Mahout Recommendation Systems Introduction to recommender systems Representing recommender data Production level recommendation systems Distributing recommender computations Clustering Review of clustering Representing data Clustering algorithms reviewed Evaluating and improving clustering quality Clustering in production Case studies in clustering Classification Introduction to classification Training a classifier Evaluating and tuning a classifier Deploying a classifier Case studies in classification Emerging trends in applications of large scale data processing and machine learning
Additional Information:This module provides both theoretical and practical experience of large scale data storage considerations and the use of tools to support the processing of that data.

Assessments

Report
Technology