Course Catalogue

Artificial Intelligence with Engineering Applications UESTC3036

  • Academic Session: 2026-27
  • School: School of Engineering
  • Credits: 10
  • Level: Level 3 (SCQF level 9)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

This course introduces AI (mainly evolutionary optimization and machine learning) knowledge and its applications in electronics and electrical engineering. It also cultivates students' skills in modeling engineering problems using computer programmes, employing software tools with embedded AI techniques to solve them, and implementing fundamental AI techniques through these programmes. The course is also helpful for students' final-year projects and for employability.  

Timetable

Weekly online lectures (12 weeks): 90 minutes of asynchronous micro-lecture videos (5-10 minutes each) to be completed before the weekly catch-up session.

Catch-up demonstration session (12 weeks): 1-hour per week, small groups (~40 students), facilitated on campus. 

There will be 4 on-campus teaching blocks. Each block comprises further support complementing online provision (a 1-hour review lecture + 1-hour seminar + 1-hour tutorial) plus 2 x 1-hour labs.

Requirements of Entry

None

Excluded Courses

None

Co-requisites

None

Assessment

(a) Written Exam - 1 Final exam (60%) This written exam will focus on AI knowledge and working principles of AI algorithms [ILO 1, 2]. 

(b) Laboratory report outcomes (25%) The lab reports will test the practical knowledge of employing and implementing AI algorithms, and assessing the result of the AI algorithms [ILO 3, 4].

(c) Oral assessments (15%) The oral assessments will focus on AI knowledge and working principles of AI algorithms, happening in the weekly catch-up demonstration sessions [ILO 1, 2].

Main Assessment In: December

Course Aims

This course aims to introduce fundamental concepts in AI (mainly evolutionary optimization and machine learning) and equip students with popular AI algorithms, cultivate the ability to implement standard AI algorithms through computer programmes, and the ability to select and employ AI algorithms to solve engineering problems using existing AI tools/toolboxes, and analyze the results.

Intended Learning Outcomes of Course

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

■ demonstrate their understanding of fundamental AI (mainly evolutionary optimization and machine learning) concepts

■ demonstrate their understanding of the working principles of AI algorithms for classification, regression, and optimization;

■ develop, implement, and critically evaluate the performance of AI algorithms.

■ model engineering problems and link them with AI techniques using computer programmes. Select appropriate AI techniques to solve engineering problems and analyze the results properly.

Minimum Requirement for Award of Credits

No exceptions