This advanced course teaches students about modern Artificial Intelligence techniques to address complex engineering problems. The course introduces modern techniques in artificial intelligence, including deep learning and provides an overview of advanced programming libraries in a variety of domains, including best practices in training and evaluating AI system.

■ 2-3h/week lectures

■ 2h/week presentations weeks 3-7

■ 3h/week lab weeks 8-10 + 1h lab intro

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Individual assessments:

- 2 x quiz delivered online through Moodle on the theoretical knowledge acquired by the students, each 10%

- 2 x coding exercise submitted online through Moodle, each 15%

- Individual report (20%) on strategy for solving a AI problem (approach to coding and to benchmarking the result)

Group assessment:

- development, implementation, training and benchmarking AI algorithm for problem: presentation and practical demonstration (30%)

Reassessments are normally available for all courses, except those which contribute to the Honours classification. Where, exceptionally, reassessment on Honours courses is required to satisfy professional/accreditation requirements, only the overall course grade achieved at the first attempt will contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The group component of this course (30% presentation and demonstration) cannot normally be reassessed on an individual basis, due to the nature of the group work integral to this part.

This course aims to present students with modern techniques in artificial intelligence, including deep learning, to address large multidimensional datasets and complex data.

The course will evaluate AI in the context of sustainability and responsible innovation. Specific aspects related to energy demand, computational cost and infrastructure impact of AI will be presented and discussed with the help of invited experts from the industry sector.

The course focusses on the application of AI (Deep Learning and Machine Learning) techniques to engineering problems. The students will be introduced to the use of advanced libraries in a variety of domains as well as providing good practice in training and validating models.

The course will include practical AI case studies.

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

■ Evaluate the impact of AI on our society, in terms of resources consumption and long-term sustainability of the solutions.

■ Design, build, train and apply fully connected deep neural networks to address a specific task.

■ Determine and judge the strengths and weaknesses of various AI/ML algorithms and propose appropriate choices for specific problems.

■ Evaluate a complex engineering application and create a suitable AI-driven solution to address it.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.

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.