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