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School of Engineering and Informatics (for staff and students)

Fundamentals of Machine Learning (G6061)

Fundamentals of Machine Learning

Module G6061

Module details for 2021/22.

15 credits

FHEQ Level 5

Pre-Requisite

some programming experience

Module Outline

This module provides an introduction to the important field of machine learning. A systematic approach will be used based on the following three key ingredients: tasks, models and features. Students will be introduced to both regression and classification and concepts such as model performance and learnability will be emphasized. Taught techniques will include: linear regression, single and multiple layer perceptron classification, kernel-based models (including RBF and SVM), decision tree models and random forest, naïve bayes classification and k-means clustering. Techniques for pre-processing of the data (including PCA) will be introduced. Throughout, an example-based approach will be adopted.

Module learning outcomes

Demonstrate basic knowledge of several supervised and unsupervised machine learning models including multi-layer perceptron, support vector machine, random forest, K-means, and PCA.

Map machine learning models to tasks based on reasoned arguments.

Explain and exploit practical concepts such as cross-validation and learning curve.

Use machine learning toolboxes to solve classification/regression problems with real-world data, including pre-processing of the data and incorporating prior knowledge.

TypeTimingWeighting
Coursework100.00%
Coursework components. Weighted as shown below.
ReportA2 Week 2 80.00%
Computer Based ExamT2 Week 5 (1 hour)10.00%
Computer Based ExamT2 Week 9 (1 hour)10.00%
Timing

Submission deadlines may vary for different types of assignment/groups of students.

Weighting

Coursework components (if listed) total 100% of the overall coursework weighting value.

TermMethodDurationWeek pattern
Spring SemesterLecture1 hour22222222222
Spring SemesterLaboratory1 hour11111111111

How to read the week pattern

The numbers indicate the weeks of the term and how many events take place each week.

Dr Benjamin Evans

Assess convenor
/profiles/555479

Dr Johanna Senk

Assess convenor
/profiles/589762

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The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.

School of Engineering and Informatics (for staff and students)

School Office:
School of Engineering and Informatics, СÀ¶ÊÓƵ, Chichester 1 Room 002, Falmer, Brighton, BN1 9QJ
ei@sussex.ac.uk
T 01273 (67) 8195

School Office opening hours: School Office open Monday – Friday 09:00-15:00, phone lines open Monday-Friday 09:00-17:00
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