Machine Learning and Optimisation
|Unit level:||Level 2|
|Teaching period(s):||Semester 1|
|Offered by||School of Computer Science|
|Available as a free choice unit?:||Y
- COMP14112 - Fundamentals of Artificial Intelligence (Compulsory)
- MATH10111 - Foundations of Pure Mathematics B (Compulsory)
Additional RequirementsStudents who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.
To enrol students are required to have taken COMP11120 and COMP14112. Or, if you are on a Computer Science and Maths programme you must have taken MATH10111.
To introduce methods for learning from data, and provide the necessary mathematical background to enable students to understand how the methods work, how to evaluate the performance a machine learning system and how to get the best performance from them. This course covers basics of both supervised and unsupervised learning paradigms and is pitched towards any student with a mathematical or scientific background who is interested in adaptive techniques for learning from data as well as data analysis and modelling.
OverviewThe world is filling up with data - billions of images online, billions of supermarket transactions, billions of events pouring out of our everyday lives. Machine Learning is about designing algorithms capable of automatically learning patterns from this supplied data. Examples of this are in online shopping like Amazon.com - which learns what products you like to buy, or in spam detection systems, which learn what spam looks like as you tag it in your spam folder.
In this course unit we will introduce you to the basics of these algorithms, implementing a basic spam filter and a handwriting recognition engine.
Teaching and learning methods
20 in total, 2 per week
2 hours of self revision
10 hours in total
Learning outcomes are detailed on the COMP24111 course unit syllabus page on the School of Computer Science's website for current students.
- Analytical skills
- Project management
- Problem solving
- Written communication
- Written exam - 60%
- Practical skills assessment - 40%
- Machine Learning Basics
- K Nearest Neighbour Classifier
- Linear Classification/Regression
- Logistic Regression
- Support Vector Machine
- Deep Learning Models
- Generative Models and Naïve Bayes
- Basics of Clustering Analysis
- K-mean Clustering
- Hierarchical and Ensemble Clustering
- Cluster Validation
COMP24111 reading list can be found on the School of Computer Science website for current students.
Face to face marking of all project work in lab
- Assessment written exam - 2 hours
- Lectures - 22 hours
- Practical classes & workshops - 12 hours
- Independent study hours - 64 hours