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CSC8644 : Practical Machine Learning for Data Analytics

  • Offered for Year: 2023/24
  • Module Leader(s): Dr Tong Xin
  • Lecturer: Dr Tatiana Alvares-Sanches
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus

Your programme is made up of credits, the total differs on programme to programme.

Semester 1 Credit Value: 10
ECTS Credits: 5.0
European Credit Transfer System


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 completion101:0010:00Lecture follow up
Structured Guided LearningLecture materials101:0010:00Asynchronous online delivery (primarily videos) of core concepts
Scheduled Learning And Teaching ActivitiesLecture112:0022:00PIP or synchronous online lecture
Structured Guided LearningAcademic skills activities111:0011:00PIP or synchronous online practical
Guided Independent StudyProject work371:0037:00coursework
Guided Independent StudyIndependent study101:0010:00Background Reading
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

Other Assessment
Description Semester When Set Percentage Comment
Report1M100Extended Technical project report.
Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Description Semester When Set Comment
Written exercise1MA written report presenting the exercises performed during the practical sessions.
Assessment Rationale And Relationship

Coursework assessments are through individual 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.

For the extended technical project, the student can choose one from the existing project pool or define their own project. This project can assess the students’ modelling skills when facing real-world challenging problems.

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