MS2S24 - Fundamentals of Machine Learning 01 Sep 2021 - 31 Aug 2027 | Version 1

Associated Module Information

Module Code: MS2S24
Module Title: Fundamentals of Machine Learning
Faculty: Faculty of Computing, Engineering and Science
Faculty Group: Computing and Mathematical Sciences
Faculty Sub Group: Mathematical Sciences
Module Leader: Ieuan Griffiths, Ian Fitzell
Module Team: Joel Harris
First Intended Intake: SEP 2021 Final Year of Intake: 2024
Date Closed:
Credit Value: 20 Credit Level: 5
Language: English
Percentage of Module Taught in Welsh: 0
Equivalent Module:
HECOS codes:
HECOS Code Weighting:

Document Version Information

Version 1
Valid From 01 Sep 2021
Valid To 31 Aug 2027

Module Aims

To provide students with an understanding of statistical learning.

To provide students with an understanding of using software for implementing machine learning algorithms and interpret results.

Content Summary

Machine Learning:

Supervised and Unsupervised Learning, Regression and Classification. Metric, Overfitting and Underfitting, Bias-Variance trade-off, no free-lunch theorem.

Gradient Descent:

Minimisation problems, estimation and use of the gradient, step size, stochastic gradient descent. Implementation of the Stochastic Gradient Descent algorithm on a computer.

Regression:

Simple Linear Regression, Multiple Regression, Assumptions of the least squares model. Implementation of regression from scratch and using statistical software libraries. Software tools for regression.

Classification:

k-NN, Naïve Bayes, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting. Software tools for classification.

Learning and Teaching Methods

Activity Type Hours
Lecture 10
Practical classes and workshops 10
Supervised time in studio/workshop 6
Work based learning 74
Directed Study 28
Formative Assessment - Independent 72
Total Hours Selected 200

Learning Outcomes

# Learning Outcome
LO1 Understand the theory of statistical learning.
LO2 Select, implement and apply machine learning algorithms in practical applications.

Module Requisites

N/A

Assessment Criteria

Assessment Category Assessment Type Description Duration Word Count Weight (%) Best of? Pass Mark
Asynchronous Assessment Portfolio 1 Portfolio of exercises 0 5000 100 No 40

Assessment Matrix

Assessment Type Learning Outcomes
LO1 LO2
Portfolio 1 ✔ ✔

Reading List

Ben-David, S., & Shalev-Shwartz, S. (2014). Understanding Machine Learning. Cambridge University Press.

Bishop, C. M. (2007). Pattern Recognition and Machine Learning. Springer-Verlag.

Grus, J. (2019). Data Science from Scratch 2nd Edition. O’Reilly.

Mirjalili, V., & Raschka, S. (2019). Python Machine Learning. Springer.