Mathematics for Machine Learning and Artificial Intelligence (MATH0114)

Key information

Faculty: Faculty of Mathematical and Physical Sciences
Teaching department: Mathematics
Credit value: 15

Restrictions: This module is normally intended for students in year 3 of a mathematics degree. The normal prerequisites are MATH0003/4/5/6/11/14 with MATH0057 recommended.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

This module will introduce the student to the theoretical foundations behind some of the most widespread methods used in Machine Learning and Artificial Intelligence. We will dive deeply into the mathematical foundations of three learning paradigms, embodied in one flagship method within each one: (i) Linear regression for supervised learning, (ii) principal component analysis for unsupervised learning, and (iii) backpropagation for deep learning. Additionally, we will study the mathematics behind diffusion models, currently one of the most notable generative AI methods to produce images from text. Besides the theoretical aspects of these techniques, students will be exposed to practical implementation of machine learning algorithms through practical examples shown during lectures. Online tutorials on the coding (in Python) of the studied methods will be provided.

Module deliveries for 2024/25 academic year

Intended teaching term: Term 2 听听听 Undergraduate (FHEQ Level 6)

Teaching and assessment

Mode of study: In person
Methods of assessment: 10% Coursework

90% Exam
Mark scheme: Numeric Marks

Other information

Number of students on module in previous year: 0
Module leader: Dr Alejandro Diaz De La O
Who to contact for more information: math.ugteaching@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

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Important information

The catalogue has been updated with key information about the modules that will run during the 2024/25 academic session. Current and prospective students can browse through the catalogue to consider possible module choices for the coming year.

Please note,听information in the catalogue is听subject to change as teaching and assessment arrangements for 2024/25 may need to be adjusted in line with the University's Feedback and Assessment principles and operating model.

Centrally managed exam durations will be provided on students' individual exam timetables. Arrangements for locally managed exams and in-class activity will be confirmed by the teaching department for the module.

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