2020/21 Taught Postgraduate Module Catalogue
COMP5611M Machine Learning
15 creditsClass Size: 230
Module manager: Dr Matteo Leonetti
Email: M.Leonetti@leeds.ac.uk
Taught: 1 Sep to 31 Jan (adv yr), Semester 1 (Sep to Jan) View Timetable
Year running 2020/21
This module is not approved as an Elective
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 type | Number | Length hours | Student hours |
| Lecture | 22 | 1.00 | 22.00 |
| Practical | 1 | 2.00 | 2.00 |
| Practical | 9 | 2.00 | 18.00 |
| Tutorial | 11 | 1.00 | 11.00 |
| Private study hours | 97.00 | ||
| Total Contact hours | 53.00 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Practical | Programming Test | 10.00 |
| Practical | Programming Test | 10.00 |
| In-course Assessment | Online test (Gradescope) | 25.00 |
| In-course Assessment | Online test (Gradescope) | 25.00 |
| Total percentage (Assessment Coursework) | 70.00 | |
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 type | Exam duration | % of formal assessment |
| Online Time-Limited assessment | 48 hr 00 mins | 30.00 |
| Total percentage (Assessment Exams) | 30.00 | |
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 websiteLast updated: 18/09/2020 12:25:42
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