Technology and Skills for Engineering the Future ENG5333

  • Academic Session: 2024-25
  • School: School of Engineering
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Repeated in Semesters 1 and 2
  • Available to Visiting Students: Yes
  • Taught Wholly by Distance Learning: Yes
  • Collaborative Online International Learning: No

Short Description

The engineering industry is a diverse sector where many different approaches are required to resolve complex engineering problems. This course introduces important techniques and technologies, such as programming embedded processors, robotics, artificial intelligence and machine learning, AR/VR, and how they can work together to produce solutions to complex engineering problems.

Timetable

Course consists of 10 weeks and all learning will be delivered on-line using a combination of videoed lectures, activities and reading. This course is taught totally on-line.

Requirements of Entry

Mandatory Entry Requirements

None

Recommended Entry Requirements

Excluded Courses

none

Co-requisites

none

Assessment

Course participants will take a project-based summative assessment in which they will individually write a computer program and develop an object detection system using machine learning techniques. The course participants will be required to come up with a Gantt chart detailing the timeline and components of the project. At the end of the module, the course participant will submit a project report which will be assessed against with the following criteria:

1. Project planning (including Gantt chart and bill of materials) - 10 %

2. Quality of the computer programming code (i.e. correctness, documentation) - 15 %

3. Implementation of machine learning technique - 15 %

4. Project Report - 60 %

Are reassessment opportunities available for all summative assessments? No

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Course Aims

The aims of this course are to:

■ provide the students with the skills and knowledge necessary to program embedded processors

■ introduce students to operating principles of typical robotics systems and their applications

■ introduce students to machine learning and AR/VR techniques  

■ provide insight into professional practice for engineering projects

Intended Learning Outcomes of Course

By the end of this course students will be able to:

 

■ write computer programs to control embedded processors in realistic engineering scenarios;

■ compare current machines learning approaches, and design and test code for a simple machine learning algorithm;

■ apply a systematic design process to the development of a robotic / autonomous system;

■ evaluate the worth of AR/VR techniques in solving specific real-world engineering problems; 

■ state the key aspects of modern engineering project management standards, and use these in engineering projects.

Minimum Requirement for Award of Credits

Students must submit all items of the course's summative assessment.