DSC8005 : Deep Learning and Computer Vision
- Offered for Year: 2025/26
- Module Leader(s): Dr Stephen McGough
- Lecturer: Dr Deepayan Bhowmik
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
• Introducing the fundamentals of Deep Learning and Computer Vision:
• The core concepts behind these areas Deep Learning and Computer Vision.
• Applications of Deep Learning and Computer Vision in real world scenarios.
• The standard toolkits used for Deep Learning and Computer Vision.
• How to appropriately apply these toolkits to real-world problems.
• Make students fluent in the approaches of Deep Learning and Computer Vision which will allow them
to build up the skills to enable them to apply these skills for applications in companies, academia
or the third sector.
• Know the limits and ethical impacts of using such tools to enable them to make appropriate decisions
when working.
• Make students aware of good practices and how to ensure their results are reported in a fair and
honest manner
Outline Of Syllabus
Material will cover such topics as, whilst also including new and emerging areas of research:
• Multi-Level Perceptrons (MLP).
• Backpropagation, gradient decent and optimization.
• Convolutional Neural Networks (CNN).
• Better training of neural networks, e.g. Preventing Overfitting, Dropout, Transfer Learning,
Data Augmentation.
• Ethics and Challenges of Deep Learning, Adversarial Examples, and good practices in Deep Learning.
• Interoperability and Explainability of Deep Learning.
• Fundamental of Image acquisition, Sampling, Image Quality, Image Pixel Relationships.
• Linear Operators, Transforms, Filters and Convolution.
• Object Classification, and Detection.
• Scene Segmentation.
• Pattern Recognition
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Preparation and completion of the Deep Learning assignment |
| Guided Independent Study | Assessment preparation and completion | 11 | 2:00 | 22:00 | Lecture follow-up includes time for the formative practical/tutorial exercises |
| Guided Independent Study | Assessment preparation and completion | 1 | 0:30 | 0:30 | Oral Examination |
| Guided Independent Study | Assessment preparation and completion | 1 | 4:30 | 4:30 | Preparation for oral examination |
| Guided Independent Study | Assessment preparation and completion | 1 | 30:00 | 30:00 | Preparation and completion of the Computer Vision assignment |
| Guided Independent Study | Directed research and reading | 22 | 1:00 | 22:00 | Pre-recorded online materials to aid learning |
| Scheduled Learning And Teaching Activities | Practical | 24 | 1:00 | 24:00 | Practical sessions (In-person) |
| Scheduled Learning And Teaching Activities | Small group teaching | 22 | 1:00 | 22:00 | Tutorial sessions (in-person) |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 2 | 1:00 | 2:00 | Practical sessions (in-person) |
| Guided Independent Study | Independent study | 1 | 43:00 | 43:00 | Independent background reading |
| Total | 200:00 |
Teaching Rationale And Relationship
Lectures materials are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work.
Lecture follow-up, e.g., quizzes and exercises, is associated with each lecture to provide sufficient hands-on training and rapid feedback on understanding.
Scheduled sessions are used both for solution of problems and work requiring extensive computation to give insight into the ideas/methods studied
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Practical/lab report | 2 | M | 50 | Deep Learning Assignment (max 1000 lines of code) |
| Practical/lab report | 2 | M | 50 | Computer Vision Assignment (max 1000 lines of code) |
Zero Weighted Pass/Fail Assessments
| Description | When Set | Comment |
|---|---|---|
| Oral Presentation | M | Presentation and demonstration of the methods and results from the practical/lab reports (30 mins) |
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 | 2 | M | Practical/Tutorial exercises. Approx one per practical session |
Assessment Rationale And Relationship
The assignments test the students’ ability to apply deep learning and computer vision techniques in a reproducible manner, using effective tools and methods to solve a real-world challenge.
The oral exam is a pass/fail component to validate that the student has understood the key aims of the module.
The formative assessment facilitates a reflective learning. The 'teachers' answer to each exercise is released two weeks after assessment is set with students expected to talk within the practical session about anything which is not understand how their answer differs
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC8005's Timetable