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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.

TypeHours
Lecture24
Follow-up work12
Completion of assessment task64
Preparation for scheduled sessions12
Wider reading or practice38
Total study time150

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

MethodPercentage contribution
Individual Oral Presentation 10%
Individual report  ( words) 80%
Peer review 10%

Repeat

MethodPercentage contribution
Technical report 100%

Referral

MethodPercentage contribution
Technical report 100%

Repeat Information

Repeat type: Internal & External

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