Module Information

Module Identifier
CS36110
Module Title
Machine Learning
Academic Year
2017/2018
Co-ordinator
Dr Neil Mac Parthaláin
Semester
Semester 1
Other Staff
 

Course Delivery

Delivery Type Delivery length / details
Lecture 20 x 1 Hour Lectures
 

Assessment

Assessment Type Assessment length / details Proportion
Semester Assessment Report  1500 Words - Report given to a machine learning task, select two machines learnig techniques and discuss their applications to this task.  40%
Supplementary Assessment Supplementary Assessment  Students must resit failed assessment.  100%
Semester Assessment Report and Application  1500 Words - Experimentation and report  60%

Learning Outcomes

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

1. Describe important, different machine learning techniques and algorithms
2. Discuss properties and limitations of different machine learning techniques and algorithms
3. Compare different machine learning techniques and algorithms
4. Apply different machine learning techniques and algorithms and evaluate them experimentally
5. Select an appropriate machine learning technique for a given application

Brief description

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

Content

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

2. Decision Trees (approx. 3 lectures)
Introduction to decision trees (classification trees and regression trees); over-fitting; pruning; application example

3. Bayesian Learning (approx. 4 lectures)
Bayes' theorem/rule; maximum likelihood; maximum a posteriori; naive Bayes classifier; application example

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

5. Support Vector Machines (approx. 4 lectures)
maximum margin hyperplane; kernel trick; kernel functions; soft margins; application example

6. Selected Additional ML Topics (approx.. 4 lectures)
Introduction and discussion of another important machine learning technique topic, e.g., reinforcement learning, handling uncertainty, genetic programming

7. Summary and Revision (2 lectures)

Module Skills

Skills Type Skills details
Application of Number Inherent to machine learning
Communication Report writing for assessment.
Improving own Learning and Performance Inherent in practical assignment: application of an ML technique and self-assesment via test data.
Information Technology Inherent to the module
Personal Development and Career planning Machine learning techniques find numerous applications in the real world, the selection of the module does expand the areas for personal development and career planning in both industry, government bodies and academia.
Problem solving Inherent in both assignments: selection of an appropriate technique to solve an ML problem.
Research skills Inherent in practical assignment: application of an ML technique.
Subject Specific Skills Application of ML techniques to various problems.
Team work

Notes

This module is at CQFW Level 6