2016/17 Undergraduate Module Catalogue
COMP3611 Machine Learning
10 creditsClass Size: 80
Module manager: Matteo Leonetti
Email: M.Leonetti@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2016/17
Pre-requisites
| COMP2611 | Artificial Intelligence |
This module is not approved as a discovery module
Objectives
On completion of this module, students should be able to:• understand the principal representations and algorithms used in machine learning
• 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:
Decision trees, Bayesian networks, instance-based learning, kernel machines, clustering, reinforcement learning inductive logic programming, artificial neural networks, deep learning.
Methods for evaluating performance.
Examples will be drawn from simple problems that arise in studies of machine language understanding and 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 |
| Private study hours | 58.00 | ||
| Total Contact hours | 42.00 | ||
| Total hours (100hr per 10 credits) | 100.00 | ||
Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Practical | Practical | 10.00 |
| Practical | Practical | 10.00 |
| Total percentage (Assessment Coursework) | 20.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Exams
| Exam type | Exam duration | % of formal assessment |
| Open Book exam | 2 hr 00 mins | 80.00 |
| 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 websiteLast updated: 07/09/2016
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