| Unit name | Special Topics in Artificial Intelligence and Deep Learning |
|---|---|
| Unit code | SCIFM0002 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Fey |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
120CP of level H units in “X with Computing/Scientific Computing” |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Chemistry |
| Faculty | Faculty of Science |
Why is this unit important?
The aim of this unit is to give students a broad understanding of modern techniques and concepts of artificial intelligence and deep learning, with applications in scientific computing and problem solving.
How does this unit fit into your programme of study
This unit is intended for students in the 4th year of the “X with Computing/Scientific Computing” degrees.
An overview of content
Topics covered include the following.
How will students, personally, be different as a result of the unit
Combining coding skills with an understanding of cutting-edge techniques in AI and deep learning, as well as an expertise in handling multi-dimensional data is transformative in increasingly diverse fields. You will be able to tackle conceptually challenging or time-consuming tasks that other students cannot, increasing your career options and employability. At this level, you will also be able to work independently, suggesting creative computing and data-led solutions to scientific problems, and supporting others in implementing them.
Learning Outcomes
After completing this unit, students should be able to:
The unit will be delivered using a flipped classroom approach, with most teaching through asynchronous materials delivered via a VLE, using the face-to-face interactions to support problem-based learning at regular drop-in sessions. Feedback will be provided for coursework and formal assessments.
Tasks which help you learn and prepare you for summative tasks (formative):
Formative assessment is built into every aspect of this practice-based course.
Tasks which count towards your unit mark (summative):
Summative assessment will be through two online tests (20%, ILO’s 1-5) and two programming projects (80%, ILO’s 1-5).
When assessment does not go to plan
If you are unable to complete successfully the assessment for the unit, either because of exceptional circumstances or through academic failure, you will be set a single alternative synoptic assessment to test all of the intended learning outcomes of this unit on an appropriate reassessment timescale.
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. SCIFM0002).
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 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.
