Machine Learning on Big Data

Summary of module for applicants:

This module aims to provide students with the core theoretical and practical background required for using machine learning techniques for big data analytics, and developing large-scale intelligent algorithms for big data systems.

Main topics of study:

• Introduction to Data Mining for Big Data
• Big Data Processing
• Big Data Classification
• Big Data Clustering
• Machine Learning applications for Big Data Analytics
• Legal, Social, and Ethical issues within Machine Learning on Big Data

Learning Outcomes for the module

• Digital Proficiency - Code = (DP)
• Industry Connections - Code = (IC)
• Emotional Intelligence Development - Code = (EID)
• Social Intelligence Development - Code = (SID)
• Physical Intelligence Development - Code = (PID)
• Cultural Intelligence Development - Code = (CID)
• Cognitive Intelligence Development – Code = (COI)
• Community Connections - Code = (CC)
• UEL Give-Back - Code = (UGB)

At the end of this module, students will be able to:

Knowledge

• Explain the current and emerging concepts, technologies and principles relevant to Big Data analytics using data mining and machine learning. (DP)
• Illustrate understanding of the current advanced techniques, tools and methods applicable on Big Data analytics using machine learning. (DP)

Thinking skills

• Examine approaches for preparing Big Data from different and heterogeneous data sets for analysis. (COI)
• Critically evaluate appropriate machine learning techniques within a business context, which includes legal, social, and ethical issues. (COI)

Subject-based practical skills

• Design, implement, and test a machine learning-based system working with Big Data. (DP)
• Utilise batch and real-time machine learning analysis on Big Data and their visualisations effectively. (DP)

Skills for life and work (general skills)

• Participate in the peer review process exercise for assessing project outcomes. (DP)
• Work effectively in groups to develop a software project and present it to technical and non-technical audiences. (DP, SID)