CSC8635 : Machine Learning with Project
- Offered for Year: 2024/25
- Module Leader(s): Dr Stephen McGough
- Lecturer: Professor Jaume Bacardit, Dr Wanqing Zhao
- Teaching Assistant: Mrs Chinomnso Ekwedike
- 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 – 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
- Natural Language Processing
- Data preprocessing
- 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 delivery |
| Guided Independent Study | Assessment preparation and completion | 30 | 1:00 | 30:00 | Coursework - project work |
| Scheduled Learning And Teaching Activities | Practical | 6 | 2:00 | 12:00 | Practical (PIP) |
| Scheduled Learning And Teaching Activities | Workshops | 9 | 1:00 | 9:00 | Group problem classes to go over the lecture material. Present in person. Q&A |
| Guided Independent Study | Independent study | 15 | 1:00 | 15:00 | Background Reading |
| Guided Independent Study | Independent study | 26 | 1:00 | 26:00 | Lecture follow up |
| Total | 100:00 |
Jointly Taught With
| Code | Title |
|---|---|
| CSC8111 | Machine Learning |
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 |
|---|---|---|---|---|
| Report | 1 | M | 100 | Extended 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 |
|---|---|---|---|
| Prob solv exercises | 1 | M | A set of 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
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC8635's Timetable