CSC8111 : Machine Learning with Project
- Offered for Year: 2025/26
- Module Leader(s): Dr Stephen McGough
- Lecturer: Professor Jaume Bacardit, Dr Wanqing Zhao
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
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 | |
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 primarily 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.
• Natural Language Processing.
• Data pre-processing.
• Interpretability, fairness, and ethics of Machine Learning.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 8 | 1:00 | 8:00 | Lectures (in-person). |
| Guided Independent Study | Assessment preparation and completion | 33 | 1:00 | 33:00 | Coursework. |
| Scheduled Learning And Teaching Activities | Practical | 8 | 2:00 | 16:00 | Practical (in person) plus formative exercises. |
| Guided Independent Study | Independent study | 22 | 1:00 | 22:00 | Lecture follow up. |
| Guided Independent Study | Independent study | 21 | 1:00 | 21:00 | Background Reading. |
| Total | 100:00 |
Jointly Taught With
| Code | Title |
|---|---|
| CSC8635 | Machine Learning with Project |
Teaching Rationale And Relationship
Pre-recorded lectures provide maximum flexibility for students learning new material. In-person small group teaching classes allow students to check their understanding and gain support for the material.
The practical classes are designed to put all concepts covered in the lectures into practice through guided practical instructions and work on the module’s coursework.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 1 | M | 100 | Extended technical Report. 2000 words. |
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 |
|---|---|---|---|
| Prob solv exercises | 1 | M | A set of short exercises conducted during the practical sessions. |
Assessment Rationale And Relationship
The summative coursework is through an individual deliverable (Report/code), emphasising both the conceptual and applied nature of the module. Students will work on a small set of practical tasks, where they will set up and evaluate machine learning solutions that fulfils certain specified criteria.
Through the module students will complete a set of short exercises (formative assessment) conducted during the practical sessions. The student will be assessed on their understanding of machine learning as well as data processing skills, and the use of standard tools.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC8111's Timetable