CSC3834
Introduction to Artificial Intelligence
- Offered for Year: 2025/26
- Module Leader(s): Dr Deepayan Bhowmik
- Owning School: School of 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 |
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.
Learning Outcomes
Intended Knowledge Outcomes
The students should:
- Be aware of commonly-used techniques in AI including generative AI models, including large language models (LLMs) and their capabilities.
- Be knowledgeable of core AI concepts, including deep learning, machine learning, and neural networks.
- Have a working knowledge of which techniques are useful and appropriate for a particular problem in AI’s application across domains such as natural language processing (NLP), computer vision, and robotics,
- Possess the knowledge of how the different techniques are implemented in common programming frameworks
- Have an appreciation of the ethical obligations required when performing AI.
- Understanding the role of AI in the society and businesses.
Intended Skill Outcomes
The student should:
- Be able to understand the concepts behind AI.
- Discuss and communicate the ability and limitations of AI approaches.
- Identify the most appropriate AI techniques for a given problem.
- Be able to assess risks/limitations associated with applications of AI.
- Be able to identify and quantify sources of bias and use appropriate mechanisms to reduce bias.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 11 | 2:00 | 22:00 | Some lecture materials maybe pre-recorded. Lectures are in Present in Person (PiP) and where possible also streamed live online |
| Scheduled Learning And Teaching Activities | Practical | 11 | 2:00 | 22:00 | 1x2 hour drop in practical per week. PiP mode |
| Guided Independent Study | Project work | 66 | 1:00 | 66:00 | Practical coursework and portfolio preparation |
| Guided Independent Study | Project work | 5 | 1:00 | 5:00 | Reflective report preparation |
| Guided Independent Study | Independent study | 22 | 1:00 | 22:00 | Lecture follow-up |
| Guided Independent Study | Independent study | 63 | 1:00 | 63:00 | Background reading, guided reading, one article, chapter or equivalent per two weeks |
| Totals | 200 |
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 maybe be pre-recorded and students have the opportunity to watch the videos ahead of the lecture. Lectures in person and where possible streamed live with recap will be available. Lecture follow up will consist of Q&A about the lecture material.
Assessment Methods
Other Assessments
| Component | Semester | When set | Percentage | Comment |
|---|---|---|---|---|
| Report 1 | 1 | M | 100 | Assessed Coursework covering Semester 1 taught material |
Formative Assessments
| Component | Semester | When set | Comment |
|---|---|---|---|
| Oral Examination 1 | 1 | 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 AI techniques, using effective tools and methods to solve a real-world challenge
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/