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 and oral presentation and discussion on the same.  20%
Semester Assessment Written assignment contrasting the use of two methods discussed in the course, applied to data provided by the student. (3000 words)  80%
Supplementary Assessment Will take a form as agreed by Departmental exam board   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¿s 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)


This module is at CQFW Level 7