Unit name | Machine Learning (Teaching Unit) |
---|---|
Unit code | COMS30035 |
Credit points | 0 |
Level of study | H/6 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Rui Ponte Costa |
Open unit status | Not open |
Pre-requisites |
COMS10016 Imperative and Functional Programming and COMS10017 Object-Oriented Programming and Algorithms I or equivalent. COMS10014 Mathematics for Computer Science A and COMS10013 Mathematics for Computer Science B or equivalent. COMS20011 Data-Driven Computer Science or equivalent. Programming: Python or another major programming language (Java, C). Maths: basic linear algebra, basic statistics, some calculus, some discrete maths. |
Co-requisites |
EITHER Assessment Units COMS30033 Machine Learning (Exam assessment, 10 credits) OR COMS30034 Machine Learning (Coursework assessment, 20 credits). Please note: COMS30035 is the Teaching Unit for the Machine Learning option. Single Honours Computer Science students can choose to be assessed by either examination (10 credits, COMS30033) or coursework (20 credits, COMS30034) by selecting the appropriate co-requisite assessment unit. Any other students that are permitted to take the Machine Learning option are assessed by examination (10 credits) and should be enrolled on the co-requisite exam assessment unit (COMS30033). |
School/department | School of Computer Science |
Faculty | Faculty of Engineering |
Machine Learning is the science of how we can build abstractions of the world from data and use them to solve problems in a data-driven way. This unit introduces the field of Machine Learning, and teaches how to create and use software that improves with experience. Examples include:
Successful completion of this unit will enable students to:
In addition, students assessed by coursework will be enabled to:
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.
Teaching will take place over Weeks 1-7, with coursework support in weeks 8-10 and for students assessed by examination, consolidation and revision sessions in Weeks 11 and 12.
Examination details:
January timed assessment (100%, 10 credits)
OR
Coursework details:
Coursework, to be completed over weeks 8-10. (100%, 20 credits)
Flach, Peter, Machine Learning: The Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012) ISBN: 978-1107096394