MIND6002 Technologies, Modules and Algorithms
Module Overview
This module focuses on the interfaces between technologies, algorithms, devices and systems. The core aim is to develop a conceptual understanding of the research challenges within and at the interfaces between the MINDS CDT themes. This will help to build a common language among the cohort, enabling them to work more effectively together. The module draws heavily on case studies and tutorials delivered by our partners in industry, government and the third sector.
Aims and Objectives
Module Aims
To gain an in-depth knowledge and understanding of the interfaces between technologies, algorithms, devices, and systems.
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Embedded AI algorithmic techniques
- Nano-electronic technologies for AI algorithms
- Agent-based adaptive systems and performance guarantees
- Task-optimised devices and systems
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Formulate research questions in hardware-enabled AI
Syllabus
Interdisciplinary research practice Challenges in embedded artificial intelligence Challenges in nano-electronic technologies for artificial intelligence Challenges in agent-based adaptive systems Challenges in task-optimised devices and systems Use cases from industry, government and third sector partners
Special Features
The module will develop the students’ understanding of interdisciplinary research questions across electronics and AI, and help to develop their skills in peer review, which is an essential skill for research.
Learning and Teaching
Teaching and learning methods
The module will focus on case-studies. Due to the innovative nature of the topics covered in the course, there are no textbooks available, and so examination of emerging examples from research and development projects are the best source of material. In this way, students will be challenged to think about the challenges that lie at the R&D interface between electronics and AI. We will include a number of invited speakers from industry through the module.
| Type | Hours |
|---|---|
| Lecture | 24 |
| Follow-up work | 12 |
| Completion of assessment task | 64 |
| Preparation for scheduled sessions | 12 |
| Wider reading or practice | 38 |
| Total study time | 150 |
Assessment
Assessment Strategy
Summative assessment: 1. An approximately 5,000 word individual report outlining in detail a research question aligned to one or more of the MINDS CDT research themes with associated references to exiting literature – 80%. 2. Presentation of their research question to the cohort and examiner – 10%. 3. Peer review of a report submitted by another student, providing constructive criticism against specific criteria – 10%. Each report will be assessed against the same criteria used in peer review. Peer reviewer comments will form part of the feedback.
Summative
| Method | Percentage contribution |
|---|---|
| Individual Oral Presentation | 10% |
| Individual report ( words) | 80% |
| Peer review | 10% |
Repeat
| Method | Percentage contribution |
|---|---|
| Technical report | 100% |
Referral
| Method | Percentage contribution |
|---|---|
| Technical report | 100% |
Repeat Information
Repeat type: Internal & External