| Unit name | Problem Solving with Artificial Intelligence |
|---|---|
| Unit code | SEMT20006 |
| Credit points | 20 |
| Level of study | I/5 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Professor. Lawry |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Principles of AI, AI in Society. |
| Units you must take alongside this one (co-requisite units) |
Methods of AI |
| Units you may not take alongside this one |
None |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
The ability to apply AI across a range of application domains is fundamental to being an AI engineer. Given an application problem with associated data, what machine learning algorithms are relevant and how do you transform the available data so that they can be used? In this unit, we will explore the general principles that can be applied to break down a data problem so as to make it accessible to different kinds of machine learning. Learning will be through practical problem solving. Students will work in groups on problems with clear stakeholder involvement and wherever possible with input from industrial and business partners. Applying AI in this way will also require an in-context assessments of ethical and regulatory issues and problem specific considerations of fairness and bias. The mandatory status of the unit reflects the centrality and importance of problem based learning in the programme.
How does this unit fit into your programme of study
The unit provides a focus for practical problem solving in year 2 of the programme bringing together the core application domains of robotics, health care, finance and engineering and gives students an opportunity to apply the knowledge, algorithms and methods that learned about and to develop the skills and experience they need to apply AI in real applications.
An overview of content
The unit will introduce principles for data analysis and processing, and explore how problems can be formulated as supervised or unsupervised machine learning using examples drawn from key application domains. Students will work in groups to solve two real-world problems. The group projects will be supplied by academic supervisors or industrial partners. They will be posed as real-word problems from an external client seeking a consultant AI engineer.
How will students, personally, be different as a result of the unit
After taking this unit, students will have the confidence to address a wide variety of industry-motivated, real-world problems from the perspective of an AI engineer. Students will have the ability to translate a description of a real-world problem with an associated data set into one that can be solved using machine learning. They will learn the art of identifying appropriate data processing and learning methods, while gaining an appreciation of the ethical, societal, and regulatory implications of their approach. Students will refine their skills in collaborative problem solving and the use of state of the art tools and environments. They will also learn how to communicate technical results to stakeholders who may not have a background in AI.
Learning outcomes
By the end of the unit, students will be able to:
The unit will have lectures and computer laboratories to focus on specific technical and non-technical skills relevant to the unit such as group work, project management, software tools and environments, and collaborating on shared code. These are to prepare for the specific skills that are needed during the projects.
Much of the project work is done independently through a combination of working individually and in groups but with regular supervision meetings with an academic supervisor.
Tasks which help you learn and prepare you for summative tasks (formative):
Throughout the group projects, you will receive formative feedback from your supervisor(s) about your progress with the project. You will be asked to prepare a project plan at an early stage in each project, and you will also receive formative feedback on this plan.
Tasks which count towards your unit mark (summative):
This unit will be assessed by two summative coursework assessing all learning outcomes: These will be
For both group projects, student engagement, individual contributions to the group project (ILO 1-4,6), and project management (ILO 5) will be assessed through the supervision meetings and through peer evaluation. These will be used to assign an individual moderated mark to each student for each group project.
When assessment does not go to plan:
If a student does not pass the unit overall and has failed one of the group projects but not both the group projects, then re-assessment of this component will take the form of a report on an individual modelling project
If a student does not pass the unit overall and has failed both of the group projects then they will be ask to undertake two parts to the re-assessment, both parts with equal weighting but where the second part is must pass:
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. SEMT20006).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
