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Module

CSC8635 : Machine Learning with Project

  • Offered for Year: 2023/24
  • Module Leader(s): Dr Stephen McGough
  • Lecturer: Professor Jaume Bacardit, Dr Wanqing Zhao, Dr Huizhi Liang
  • 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

- Paradigms of Machine Learning
- Exploratory Data Analysis
- Experimental Design
- Standard algorithms for classification, regression and clustering
- Introduction to Deep Learning
- Natural Language Processing
- Data preprocessing
- Interpretability, fairness and ethics of Machine Learning

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion191:0019:00Lecture follow up
Scheduled Learning And Teaching ActivitiesLecture161:0016:00Lectures: Asynchronous online delivery (primarily videos) of core concepts
Scheduled Learning And Teaching ActivitiesPractical161:0016:00Practical (PIP)
Scheduled Learning And Teaching ActivitiesWorkshops91:009:00Group problem classes to go over the lecture material. Present in person. Q&A
Guided Independent StudyProject work301:0030:00Coursework
Guided Independent StudyIndependent study101:0010:00Background Reading
Total100:00
Teaching Rationale And Relationship

Pre-recorded Lectures provides maximum flexibility for students learning new material. Problem classes (PIP) allow students to check their understanding and gain support for the material.

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
Description Semester When Set Comment
Report1MA 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

Timetable