DSC8052 : Computer Vision and Natural Language Processing (Inactive)
- Inactive for Year: 2026/27
- Module Leader(s): Dr Deepayan Bhowmik
- Lecturer: Dr Huizhi Liang
- Owning School: Computing
- Teaching Location: Â鶹´«Ã½ 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
To introduce students to the fundamentals of Computer Vision and Natural Language Processing (NLP) 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 some or all of the following areas:
Computer Vision Topics
• Image Sampling, Transforms, Frequency Domain Methods.
• Computer vision using classical and deep learning-based methods.
• Object Classification, Detection, Segmentation.
• Video/motion picture processing and applications
• 5. Applications (remote sensing, medical imaging, industrial machine vision etc.)
Natural Language Processing Topics
• Tokenization, POS tagging, Text Classification
• Vector Semantics, Embeddings, Language Models
• Context-Free Grammars and Parsing, Word Senses and WordNet
• Information Retrieval and Information Extraction
• Evaluation and Applications (Machine Translations, Question Answering, Dialogue systems)
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 20 | 1:00 | 20:00 | Interactive mixed mode lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 77:00 | 77:00 | Preparation and completion of main summative report |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:40 | 2:40 | Preparation and completion of oral examination |
| Guided Independent Study | Assessment preparation and completion | 1 | 0:20 | 0:20 | Completion of oral examination |
| Scheduled Learning And Teaching Activities | Practical | 20 | 1:00 | 20:00 | Interactive mixed mode practicals |
| Guided Independent Study | Skills practice | 20 | 1:00 | 20:00 | Independent study & practice on practicals |
| Guided Independent Study | Independent study | 30 | 1:00 | 30:00 | Independent study on course content |
| Guided Independent Study | Independent study | 30 | 1:00 | 30:00 | Background reading |
| Total | 200:00 |
Teaching Rationale And Relationship
Lectures explain the underlying principles of the module and technologies that support Computer Vision and NLP. 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 | An extended technical project along with algorithmic code. Word count: up to 3000 words, to include background, motivation, methodology, detailed figures demonstrating results and in-depth analysis. Code must be demonstrable. |
Zero Weighted Pass/Fail Assessments
| Description | When Set | Comment |
|---|---|---|
| Oral Examination | M | Structured discussion inc. a software/algorithm demonstration and reflection on the key learning objectives of the project work-up to 20 mins. |
Assessment Rationale And Relationship
The report tests the students’ ability to apply Computer Vision and NLP techniques, using effective tools and methods to solve a real-world challenge. The oral examination will be a structured discussion demonstrating and reflecting on key learning and objectives of the project.
Reading Lists
Timetable
- Timetable Website: