Introduction to Machine Learning

• Credit value: 15 credits at Level 5
• Convenor: Dr Cen Wan
• Assessment: problem-solving worksheets (20%) and a two-hour examination (80%)

In this module we cover the principle concepts and techniques of data analytics and how to apply them to large-scale data sets. You will develop the core skills and expertise needed by data scientists, including the use of techniques such as linear regression, classification and clustering. We will show you how to use the popular and powerful data analysis language and environment R to solve practical problems based on use cases extracted from real domains.

Indicative syllabus

• Overview of machine learning
• Basic statistics
• Linear regression
• Logistic regression
• Model evaluation
• Decision trees
• Random forests
• Artificial neural networks
• Clustering analysis

By the end of this module you will be able to:

• understand the fundamental concepts of machine learning and its applications
• demonstrate foundational knowledge of statistics and its role in machine learning
• apply linear and logistic regression techniques to real-world data
• evaluate machine learning models using appropriate metrics
• perform clustering analysis to identify patterns in data
• implement software using decision trees and random forests to carry out classification tasks
• gain introductory knowledge of artificial neural networks and their applications
• use relevant software tools to apply machine learning methods to practical scenarios
• develop problem-solving and analytical skills through the application of machine learning techniques
• demonstrate communication and teamwork skills through collaborative projects and presentations.