СÀ¶ÊÓƵ

School of Engineering and Informatics (for staff and students)

Machine Learning (934G5)

Machine Learning

Module 934G5

Module details for 2021/22.

15 credits

FHEQ Level 7 (Masters)

Pre-Requisite

Mathematics & Computational Methods for Complex Systems (817G5) or equivalent mathematical module / prior experience.

[MComp Computer Science students are required to take this module if G6078 Game Design and Development was taken in year 2].

Module Outline

This module exposes students to advanced techniques in machine learning. A systematic treatment 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, learnability and computational complexity will be emphasized. Taught techniques will include: probabilistic and non-probabilistic classification and regression methods and reinforcement learning approaches including the non-linear variants using kernel methods. Techniques for pre-processing of the data (including PCA) will be introduced. Students will be expected to be able to implement, develop and deploy the techniques to real-world problems.
Prerequisite: Mathematics & Computational Methods for Complex Systems (817G5) or equivalent mathematical module / prior experience.

(MSc Computer Science (conversion) students can only taken this module if 817G5 Mathematics & Computational Methods for Complex Systems is taken in Semester 1).

Module learning outcomes

Identify the strengths and weaknesses of state-of-the-art supervised, unsupervised, and reinforcement machine learning models including multi-layer perceptron, support vector machine, random forest, K-means, PCA, and Q-learning.

Critically analyse and implement several stochastic optimization methods ranging from stochastic gradient descent, stochastic variance reduction, to adaptive gradient methods for training machine learning models on big data.

Demonstrate knowledge of the fundamental principles of advanced machine learning models including probabilistic graphical models and statistical network models.

Apply developed classification/regression techniques with stochastic optimization to real-world problems, including extracting deep convolutional neural network features and incorporating prior knowledge.

TypeTimingWeighting
Coursework100.00%
Coursework components. Weighted as shown below.
ReportA2 Week 1 100.00%
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 SemesterLaboratory1 hour11111111111
Spring SemesterLecture2 hours11111111111

How to read the week pattern

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

Dr Temitayo Olugbade

Assess convenor
/profiles/272464

Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.

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
School Office location [PDF 1.74MB]