ACS61013 Data Modelling and Machine Intelligence
Module Description (subject to change)
All of our lives are affected by "machine intelligence" and "data models" - Google is a very visible example. But if you are a victim of identity theft, if you want a loan to buy a house or if you want to pass through immigration at an airport, a model derived from data using some form of machine learning technique will be involved.
Engineers increasingly look to machine intelligence techniques such as neural networks and other machine learning methods to solve problems that are not amenable to conventional analysis e.g. by application of Newton's & Kirchhoff's laws, and other physical principles. Instead they use measurements of system variables to compute a model of the process that can then be used in design, analysis and forecasting. System identification is a specific example of data modelling.
We will look at the underlying principles of machine learning, the advantages and limitations of the various approaches and effective ways of applying them with the aim of making you a competent practitioner.
Credits: 15 (Autumn semester)
If you have any questions about the module please talk to me during the lectures or the labs in the first instance. It is likely that other students will learn from any questions you ask as well, so don’t be afraid to ask.
Outside of lectures please contact me via email, or drop by.
By the end of the module students will be able to:
This module satisfies the AHEP3 (Accreditation of Higher Education Programmes, Third Edition) Learning Outcomes that are listed in brackets after each learning outcome above. For further details on AHEP3 Learning Outcomes, see the downloads section of our accreditation webpage.
Learning and Teaching Methods
NOTE: This summary of teaching methods is representative of a normal Semester. Owing to the ongoing disruption from Covid-19, the exact method of delivery will be different in 2020/21.
Lectures: 18 hours
Learning and Teaching Materials
Al lecture slides will be provided as handouts with space to take notes. Matlab “helper” software will be provided for the labs. All resources will be available on Blackboard (MOLE) as will vodcasts of the lectures.
Lab Exercise (15%)
Individual Project (60%)
Examples will be presented in class that provide an opportunity for you to gauge your understanding and to request clarification from the lecturer. Questions during lectures are welcomed.
Laboratory sessions provide a good opportunity for feedback and guidance on progress – this will be face-to-face & oral. Four Blackboard (MOLE) “quizzes” will take place & these will contain feedback available immediately the submission date is passed. The final assignment & examination will receive only summary feedback that you can access after they have been marked.
Students are encouraged to provide feedback during the module direct to the lecturer. Students will also have the opportunity to provide formal feedback via the Faculty of Engineering Student Evaluation Survey at the end of the module.
You can view the latest Department response to the survey feedback here.