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

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion161:0016:00Lecture follow up
Scheduled Learning And Teaching ActivitiesLecture161:0016:00Lectures
Scheduled Learning And Teaching ActivitiesPractical121:0012:00Practical sessions
Guided Independent StudyProject work361:0036:00Extended technical project coursework
Guided Independent StudyIndependent study201:0020:00Background Reading
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

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

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

Other Assessment
Description Semester When Set Percentage Comment
Report1M100Extended technical project. Word count: Up to 2,000 words.
Assessment Rationale And Relationship

Coursework assessments are through deliverables, emphasising both the conceptual and applied nature of the module. Students will work on a practical recognition task, where they will set up and evaluate a machine learning system that fulfils certain specified criteria. Through this assessment, the student can be assessed on their understanding of machine learning, data processing skills, tools as well as scientific writing.

Reading Lists

Timetable