Module Information

Module Identifier
Module Title
Academic Year
Semester 2
Other Staff

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Assessment Written analysis of scientific paper(s)  (3000 words limit), followed by an oral presentation and discussion on the same.  40%
Semester Assessment Written assignment  contrasting the use of two methods discussed in the course, applied to data provided by the student (3000 words).  60%
Supplementary Assessment Resubmission of failed/nonsubmitted components or others of equivalent value.  100%

Learning Outcomes

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

Demonstrate competence with the implementation methods and tools used in the development of the types of autonomous system considered in this scheme.

Show proficiency in building autonomous systems using the appropriate tools.

Demonstrate skills in designing, running and documenting experiments using autonomous systems.

Demonstrate capability to write a detailed project proposal.

Brief description

This module will equip students with the main concepts in Machine Learning by engaging them in seminar-based discussions on scientific papers. It will then help the students build towards a term paper, which will describe their practical investigation of the issues involved in applying two machine learning methods to an appropriate data set that they will have found.


The content will closely follow Alpaydin'r book, with additional use of Mitchell's book. The lectures will introduce the ideas, and the students will be expected to read further from the book. This will be tested by getting them to do presentations on sections of the book not covered in class.

1. What is Machine Learning? (2 hrs)

Foundations and assumptions of ML.

2. Supervised Learning. (2 hrs)

Learning from labelled examples.

3. Bayesian Decision Theory. (3 hrs)

Probability and optimality in learning.

4. Parametric Methods. (2 hrs)

5. Dimensionality Reduction. (2 hrs)

Detecting unnecessary attributes and removing them to improve accuracy.

6. Clustering. (3 hrs)

K-means, hierarchical, consensus clustering techniques.

7. Nonparametric Methods. (3 hrs)

Learning without constructing a model (esp. kNN); transductive learning.

8. Hidden Markov models. (3 hrs)

Probabilistic, structural models from data.

9. Assessing and Comparing Classification Algorithms. (3 hrs)

10. Combining Multiple Learners. (3 hrs)

Obtained improved results by combining the predictions of multiple classifiers.

11. Reinforcement Learning. (2 hrs)

Learning sequences of actions with reward.

Additional material requested by students via questionnaire. (2 hrs)

Module Skills

Skills Type Skills details
Application of Number Inherent to subject
Communication Seminar
Improving own Learning and Performance Inherent to subject
Information Technology Inherent to subject
Personal Development and Career planning Encourages students to see roles in subject for career and personal development
Problem solving Inherent to subject
Research skills Essay
Subject Specific Skills Representation and Reasoning for Intelligent Systems

Reading List

Recommended Text
Alpaydin, Ethem. (2010.) Introduction to machine learning /Ethem Alpaydin. 2nd ed. MIT Press Primo search Mitchell, Tom M. (1997.) Machine learning /Tom M. Mitchell. McGraw-Hill Primo search Witten, I. H. (2005.) Data mining :practical machine learning tools and techniques /Ian H. Witten, Eibe Frank. 2nd ed. Morgan Kaufman Primo search


This module is at CQFW Level 7