MA50290: Applied machine learning
[Page last updated: 23 October 2023]
Academic Year:  2023/24 
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: 

Assessment Summary:  CWRI 100% 
Assessment Detail: 

Supplementary Assessment: 

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:

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, multidimensional 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 multivariable calculus, Python include machinelearning packages). 3. Logistic regression and gradientdescent 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

Notes:
