Artificial Intelligence & Machine Learning UESTC3036

  • Academic Session: 2024-25
  • 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

Short Description

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

Timetable

The timetable will follow the block teaching model which is divided into 4 weeklong blocks over the full semester. During each block, 3 lectures each of 90 minutes, and a lab session of 90 minutes will be held.

Requirements of Entry

None

Excluded Courses

None

Co-requisites

None

Assessment

(a) Written Exam - 1 Final exam (75 %) This written exam will focus on AI knowledge and working principles of AI algorithms [ILO 1, 2]

(b) Lab Reports (15 %) - The lab reports will test the practical knowledge of employing and implementing AI algorithms, modelling electronics and electrical engineering problems by computer programmes, and assessing the result of the AI algorithms [ILO 3, 4]

(c) Project Output (10 %) - Results of the final AI-driven design project will be assessed [ILOs 2, 3, 4]

Main Assessment In: December

Course Aims

This course aims to introduce fundamental concepts in AI (mainly 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 analysing the result. 

Intended Learning Outcomes of Course

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

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

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

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

■ model engineering problems and link them with AI techniques by computer programmes. Make an appropriate selection of AI techniques to solve engineering problems and analyse the result properly with the awareness of security risks.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment. Also, students must attend the timetabled laboratory classes and must submit work for assessment for the course laboratory and project.

Students should attend at least 75% of the timetabled classes of the course.

 

Note that these are minimum requirements: good students will achieve far higher participation/submission rates. Any student who misses an assessment or a significant number of classes because of illness or other good cause should report this by completing a MyCampus absence report.