Principles of Machine Learning

Module code: ECS663U

Credits: 15.0
Semester: SEM1

Contact: To Be Confirmed

Machine learning lies in the intersection between statistics and computer science and can be found in many fields, from science and high-tech to applied areas such as retail and finance. Its main goal is to develop data-driven models to understand and predict the behaviour of real-world systems. Not surprisingly, machine learning skills are in high demand.

This module is an introduction to the principles of machine learning. The main concepts, approaches and tools necessary to develop and evaluate machine learning solutions will be covered following a hands-on approach. This exposure will give you a solid understanding of machine learning. Furthermore, it will allow you to go ahead and independently develop your machine learning skills further and to critically analyse any future developments in the field of data science.

This module covers the following key concepts and themes:

Machine learning fundamentals:

Introduction to machine learning
Methodology I and II
Supervised problems and techniques:

Regression
Classification
Unsupervised problems and techniques:

Structure analysis
Density Estimation

Recent Topics:

Modern neural networks and deep learning
Deployment

Connected course(s): UDF DATA
Assessment: 60.0% Examination, 40.0% Coursework
Level: 6