CSC8645 : Advanced AI
- Offered for Year: 2024/25
- Module Leader(s): Dr Wanqing Zhao
- Lecturer: Dr Deepayan Bhowmik, Dr Huizhi Liang, Dr Stephen McGough
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Aims
To introduce students to the advanced concepts of machine learning and computer vision, and provide the essential knowledge about the main themes, so that, in the future, they will be able to readily apply their knowledge in industry or research or further enhance it by self-study.
Outline Of Syllabus
Topics will cover contemporary areas subject to changes to follow the advances of ML, Computer Vision, and NLP domains, including but not limited the following areas
Topics will cover, but will not be limited to, some or all of the following areas:
- Machine Learning Theroy
- Based models
- Reinforcement Learning
- Fuzzy Logic
- Auto ML
- Evolutionary Computation
- Image classification
- Segmentation
- Object detection
- Vision transformer
- Federated learning in computer vision
- Generative AI and diffuser
- Deployment of computer vision models and hardware
- Applications - Satellite Imaging, Medical Imaging, Scene Understanding/Classification
- Text Mining
- Natural Language Processing (including LLMs)
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 8 | 1:00 | 8:00 | Independent study on course content |
| Scheduled Learning And Teaching Activities | Lecture | 12 | 1:00 | 12:00 | Interactive mixed mode lectures (Hybrid in person and online) |
| Guided Independent Study | Assessment preparation and completion | 20 | 1:00 | 20:00 | Background reading |
| Scheduled Learning And Teaching Activities | Practical | 20 | 1:00 | 20:00 | Interactive mixed mode practicals. 1.5 hours per week can include PiP activities. Not compulsory. |
| Guided Independent Study | Project work | 40 | 1:00 | 40:00 | Main summative assignment |
| Total | 100:00 |
Teaching Rationale And Relationship
Lectures explain the underlying principles for the module and technologies that support machine learning and computer vision. Lectures are complemented by supervised practical sessions to guide the application of these principles using suitable tools. The practical work builds up experience working with a computational toolset that is used to complete a substantive project working with data from a real-world context.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 100 | Extended technical project and code. Word count for the report; up to 1500 words, to include detailed figures demonstrating results. |
Zero Weighted Pass/Fail Assessments
| Description | When Set | Comment |
|---|---|---|
| Oral Examination | M | Structured discussion inc. a software demonstration and reflection on the key learning objectives of the project work-up to 15 mins. |
Assessment Rationale And Relationship
The report tests the students’ ability to apply machine learning and computer vision techniques, using effective tools and methods to solve a real-world challenge.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC8645's Timetable