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2019/20 Undergraduate Module Catalogue

COMP3611 Machine Learning

10 creditsClass Size: 240

Module manager: Matteo Leonetti
Email: M.Leonetti@leeds.ac.uk

Taught: Semester 1 View Timetable

Year running 2019/20

Pre-requisites

COMP2611Artificial Intelligence

This module is not approved as a discovery module

Objectives

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

• list the principal algorithms used in machine learning, and derive their update rules
• appreciate the capabilities and limitations of current approaches;
• evaluate the performance of machine learning algorithms;
• use existing implementation(s) of machine learning algorithms to explore data sets and build models.

Syllabus

Topics selected from:
Neural networks, decision trees, support vector machines, Bayesian learning, instance-based learning, linear regression, clustering, reinforcement learning, deep learning.
Methods for evaluating performance.
Examples will be drawn from simple problems that arise in studies of robotics and computer vision.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lecture221.0022.00
Practical12.002.00
Practical92.0018.00
Tutorial111.0011.00
Private study hours47.00
Total Contact hours53.00
Total hours (100hr per 10 credits)100.00

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
PracticalPractical20.00
Total percentage (Assessment Coursework)20.00

The coursework is on the application of a supervised learning algorithm to an existing, realistic, dataset. Students are asked to design an appropriate model, evaluate its performance, and analyse the effect of the parameters on the results. The coursework is implemented in python, and makes use of state-of-the-art machine learning libraries.


Exams
Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc)2 hr 00 mins80.00
Total percentage (Assessment Exams)80.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 27/09/2019

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