Gwybodaeth Modiwlau

Module Identifier

CS36110

Module Title

Fundamentals of Machine Learning

Academic Year

2022/2023

Co-ordinator

Dr Neil Mac Parthalain

Semester

Semester 1

Course Delivery

Assessment

Assessment Type	Assessment length / details	Proportion
Semester Exam	2 Hours Written Exam	100%
Supplementary Exam	2 Hours Written Exam	100%

Learning Outcomes

On successful completion of this module students should be able to:

1. Demonstrate a knowledge and understanding of the Machine Learning paradigm and the main approaches to machine learning.

2. Describe important, different machine learning techniques and algorithms and how they perform.

3. Select an appropriate Machine Learning technique and describe how this can be applied as a suitable solution for a given application problem or domain.

4. Compare and contrast the properties and limitations of different Machine Learning techniques and algorithms and discuss the implementational challenges involved in applying them.

Brief description

The module provides an introduction to the fundamentals of machine learning and a number of different machine learning techniques and algorithms. It places emphasis on the practical applications of machine learning and highlights the theoretical advantages, drawbacks and limitations of the different techniques.

Content

1. Introduction
Introduction to machine learning including example applications and classes of machine learning techniques

2. Decision Trees
Introduction to decision trees (classification trees and regression trees); over-fitting; pruning; application example

3. Bayesian Learning
Bayes' theorem/rule; maximum likelihood; maximum a posteriori; naive Bayes classifier; application example

4. Artificial Neural Networks
Introduction to perceptrons, and perceptron-based artificial neural networks; linear separability; activation functions; back-propagation; application example

5. Support Vector Machines
maximum margin hyperplane; kernel trick; kernel functions; soft margins; application example

6. Selected Additional ML Topics
Introduction and discussion of another important machine learning technique topic, e.g., reinforcement learning, handling uncertainty, genetic programming

7. Summary and Revision

Module Skills

Skills Type	Skills details
Application of Number	Inherent to machine learning
Communication	Via written examination.
Improving own Learning and Performance	Presents a general approach to machine learning. The principles can be adapted to any particular situation and are not specific to any domain.
Information Technology	Use of computing to solve real-world problems.
Personal Development and Career planning	Via in-lecture problem-solving exercises. Real-world problems will be presented as part of the taught material.
Problem solving	Via in-lecture problem-solving exercises.
Research skills	Via in-lecture problem-solving exercises.
Subject Specific Skills	Demonstrate a knowledge and understanding of the Machine Learning paradigm and the main approaches to machine learning.
Team work

Notes

This module is at CQFW Level 6