CSC3834 : Introduction to AI (Inactive)
- Inactive for Year: 2025/26
- Module Leader(s): Dr Deepayan Bhowmik
- 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: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module aims to provide foundations in the field of artificial intelligence (AI) including definition of AI, concepts and algorithms of modern AI, introduction to generative AI, applications and uses cases of AI that are transforming the modern world from health sector to finance and space to name a few.
Outline Of Syllabus
Selected topics chosen from:
• Definitions of AI.
• AI architectures (expert systems, neural network, deep learning, evolutionary computing).
• Graph search.
• Machine translation.
• Knowledge representation.
• Natural Language Processing.
• Computer vision.
• Generative AI.
• AI applications including finance, security, health, environmental, engineering, robotics.
• AI and ethics.
• AI safety, regulations and policies.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 11 | 2:00 | 22:00 | Lectures (in person). Some lecture materials maybe pre-recorded. |
| Scheduled Learning And Teaching Activities | Practical | 11 | 2:00 | 22:00 | 1x2 hour drop in practical per week (in person). |
| Guided Independent Study | Project work | 5 | 1:00 | 5:00 | Reflective report preparation. |
| Guided Independent Study | Project work | 66 | 1:00 | 66:00 | Practical coursework and portfolio preparation. |
| Guided Independent Study | Independent study | 63 | 1:00 | 63:00 | Background reading, guided reading, one article, chapter or equivalent per two weeks. |
| Guided Independent Study | Independent study | 22 | 1:00 | 22:00 | Lecture follow-up. |
| Total | 200:00 |
Teaching Rationale And Relationship
The teaching methods combine traditional lectures with practical sessions so that students can explore the topics covered in both a theoretical and practical context. Lectures outline the underlying principles, algorithms and theory, while practical lab work encourages students to implement the algorithms using rea-world data, in terms of applying the methods to real world data examples.
Lecture material may be pre-recorded and students have the opportunity to watch the videos ahead of the lecture. Lectures in person. Lecture follow up will consist of Q&A about the lecture 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 | Assessed Coursework covering Semester 1 taught material. |
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 |
|---|---|---|---|
| Oral Examination | 1 | M | Structured discussion inc. a software demonstration and reflection on the key learning objectives of the project work-up to 15 minutes. |
Assessment Rationale And Relationship
The report tests the students’ ability to apply AI techniques, using effective tools and methods to solve a real-world challenge.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC3834's Timetable