Module Catalogue

CSC8111 : Machine Learning

  • Offered for Year: 2021/22
  • Module Leader(s): Dr Yu Guan
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus
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
Structured Guided LearningLecture materials161:0016:00Lectures: Asynchronous online delivery (primarily videos) of core concepts
Guided Independent StudyAssessment preparation and completion191:0019:00Lecture follow up
Guided Independent StudyAssessment preparation and completion201:0020:00Background Reading
Scheduled Learning And Teaching ActivitiesLecture31:003:00Additional learning content delivered PIP, as well as discussion Q&A
Structured Guided LearningAcademic skills activities121:0012:00Practical Asynchronous)
Guided Independent StudyProject work301:0030:00Coursework
Total100:00
Teaching Rationale And Relationship

Lectures (in person and Asynchronous) provide the algorithmic foundations of statistical pattern recognition and the practical work seeks to build on these foundations .

Assessment Methods

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

Exams
Description Length Semester When Set Percentage Comment
Written Examination901A70N/A
Other Assessment
Description Semester When Set Percentage Comment
Report1A30a written report for the given coursework up to 6 pages
Assessment Rationale And Relationship

The written examination will primarily focus on assessing a student’s ability on understanding the basic concept, the general principles of machine learning.

The coursework is through an individual deliverable, 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 the scientific writing .

In the event the exam cannot run in the usual way (due to Covid-19) a take home 24 hours paper will be allowed.

N.B. This module has both “Exam Assessment” and “Other Assessment” (e.g. coursework). If the total mark for either assessment falls below 40%, the maximum mark returned for the module will normally be 40%.

Reading Lists

Timetable