## MA50290: Applied machine learning

[Page last updated: 23 October 2023]

Owning Department/School: Department of Mathematical Sciences
Credits: 6 [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Period:
Semester 1
Assessment Summary: CWRI 100%
Assessment Detail:
• Coursework 1 (CWRI 40%)
• Coursework 2 (CWRI 60%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: You must have familiarity with linear algebra (vectors and matrices) and multivariable calculus (especially partial derivatives and the chain rule) to take this module.
Learning Outcomes: After taking this module students will be able to:
• Formulate loss functions and test data and basic training algorithms.
• Write code to implement algorithms in Python.
• Understand the importance of algorithm efficiency.
• Understand deep neural networks and their use in machine learning.
• Conceptual understanding that enables the student to evaluate methodologies and propose new methods.

Synopsis: You will develop knowledge and understanding of Machine Learning using deep neural networks

Aims: This module will develop students' knowledge and understanding of Machine Learning by introducing them to deep neural networks and their applications.

Skills: Formulation of machine learning problems TF, applications of neural networks TF, writing code in python TFA, multi-dimensional calculus and optimization TA

Content: Machine Learning algorithms and supporting techniques and mathematics including some of the following:
1. Supervised learning and neural networks.
2. Preliminaries (linear algebra and multi-variable calculus, Python include machine-learning packages).
3. Logistic regression and gradient-descent training algorithms, with examples in Python.
4. Shallow neural networks (activation functions, training, initialisation).
5. Deep neural networks (motivation, training, forward backward propagation).
6. Applications and practical issues.

Course availability:

#### MA50290 is Compulsory on the following courses:

Department of Mathematical Sciences
• TSMA-AFM19 : MSc Mathematics with Data Science for Industry
• TSMA-AWM19 : MSc Mathematics with Data Science for Industry

 Notes: This unit catalogue is applicable for the 2023/24 academic year only. Students continuing their studies into 2024/25 and beyond should not assume that this unit will be available in future years in the format displayed here for 2023/24. Courses and units are subject to change in accordance with normal University procedures. Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules. Find out more about these and other important University terms and conditions here.