CSC3832 : Predictive Analytics and Machine Learning (Inactive)

Semester 1 Credit Value: 10
ECTS Credits: 5.0


This module aims to provide a foundation in the field of Pattern Recognition and an expertise in Machine Learning techniques as a toolkit for automatically analysing (large amounts of) data – be it static data, such as images, or dynamic data, such as time series and sensor data.

Outline Of Syllabus

•       Fundamental data representations for machine learning
•       Traditional machine learning and deep learning, basics
•       Traditional supervised learning methods: linear regression, logistic regression, naïve bayes, decision tree, random forest, support vector machines, k-nearest neighbours classifier
•       Clustering methods, k-means, expectation maximisation
•       Deep learning: multilayer perceptron, convolutional neural network, recurrent neural network, autoencoder
•       Principle component analysis, linear discriminant analysis

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture221:0022:00Lectures
Scheduled Learning And Teaching ActivitiesPractical111:0011:00Practicals
Guided Independent StudyProject work11:001:00Reflective report preparation
Guided Independent StudyProject work101:0010:00Practical coursework
Guided Independent StudyIndependent study341:0034:00Background reading
Guided Independent StudyIndependent study221:0022:00Lecture follow-up
Teaching Rationale And Relationship

The teaching methods combine traditional lectures with practical sessions so that students can explore the topics covered in both a theoretical and practical context. Lectures outline the underlying principles, algorithms and theory, while practical lab work encourages students to implement the algorithms using rea-world data, in terms of applying machine learning to predictive analysis of data.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Case study1M90topic/problem students provide a report that contributes to their portfolio of evidence of the activities (2000 words)
Report1M10A reflective report on the skills gained summarising the portfolio of evidence produced by the problem-based activities. (200 words)
Assessment Rationale And Relationship

The assessment is based on case studies, using real world data, allowing students to explore practical application of the techniques and algorithms that have been learned. The reflective report offers students the opportunity to draw together the overall learning experience on machine learning and predictive analysis of data sets.

Reading Lists