Postgraduate

Modules

Modules

CSC8635 : Machine Learning with Project

Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0

Aims

Machine Learning is concerned with the design of algorithms for recognising patterns in data. The field of pattern recognition represents the basis for a wide range of applications for automatic data analysis, such as computer vision, automatic speech recognition, or activity recognition – all based on sensor based observations of humans in their environment. The growth of “big data” means that such analysis techniques are now widely used for mining information from large amounts of data as they are collected in contemporary computing infrastructures, including clouds.

Conceptually, Pattern Recognition aims for the detection of instances of relevant classes that are typically associated with reappearing patterns in data streams. Examples of which are the automatic detection of faces in video streams, automatic transcription of spoken language, analysis of human movements, trend prediction in stock market data, intrusion detection in computer systems, or the analysis of social networks. The task is to find, model (or "learn") and classify those patterns, and to distinguish relevant from irrelevant events.
Machine Learning techniques represent the algorithmic foundation for such tasks, and involve both statistical modelling techniques and probabilistic reasoning approaches.

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

Introduction of Machine Learning: Supervised Learning, Unsupervised Learning, Data Representation, Overfitting
- Basics: Bayesian Theorem, Gaussian Distribution, Gaussian Mixture Models, Maximum Likelihood Estimation, Regularisation, Gradient Descent.
- Traditional Supervised Learning Models: Linear Regression, Logistic Regression (LR), Naive Bayes Classifier (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machines(SVM), kernel SVM, K-nearest Neighbours Classifier (KNN).
- Traditional Clustering Methods: K-Means, Expectation Maximisation (EM)
- Deep Learning: Multilayer Perceptron(MLP), Convolutional Neural Network (CNN), Recurrent Neural Network(RNN), Long Short Term Memory (LSTM), ConvLSTM. Autoencoder (AE)
- Feature Extraction Methods: Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA)

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion221:0022:00Lecture follow up
Scheduled Learning And Teaching ActivitiesLecture221:0022:00Lectures
Guided Independent StudyAssessment preparation and completion10:300:30Oral examination
Guided Independent StudyAssessment preparation and completion50:302:30Extended technical project coursework
Scheduled Learning And Teaching ActivitiesPractical121:0012:00Practical sessions
Guided Independent StudyProject work261:0026:00Extended technical project coursework
Guided Independent StudyProject work151:0015:00Coursework project one
Total100:00
Jointly Taught With
Code Title
CSC8111Machine Learning
Teaching Rationale And Relationship

Lectures explain the underpinning principles for the module and the technologies that support machine learning. Lectures are complemented by supervised practical sessions to guide the application of these principles using suitable computational tools. The practical work builds up experience working with a computational toolset that is used to complete two projects working with data from a real-world context.

Assessment Methods

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

Other Assessment
Description Semester When Set Percentage Comment
Report1M20Individual work. Word count: Up to 1,000
Report1M80Extended technical project. Word count: Up to 2,000
Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral PresentationMStructured discussion including a software demonstration and reflection on the key learning objectives of the project work.
Assessment Rationale And Relationship

Coursework assessments are through deliverables, emphasising both the conceptual and applied nature of the module. The semi structured interview facilitates a reflective discussion about how individual students have met the learning objectives of the module and how the principles of machine learning are embedded in the functionality of their project work.

Reading Lists

Timetable