Undergraduate Course: Machine Learning Practical (UG) (INFR11223)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Year 4 Undergraduate)
||Availability||Not available to visiting students
|Summary||This course follows the delivery and assessment of Machine Learning Practical (INFR11132) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11132 instead.
This course follows the delivery and assessment of Machine Learning Practical (INFR11132) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11132 instead.
Course Delivery Information
|Academic year 2022/23, Not available to visiting students (SS1)
|Course Start Date
|Learning and Teaching activities (Further Info)
Lecture Hours 12,
Supervised Practical/Workshop/Studio Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
Coursework 1 10%
Coursework 2 40%
Coursework 3 formative
Coursework 4 50%
||Summative feedback will be provided through marking of, and comments on, three pieces of assessed coursework. Detailed feedback from each coursework will be provided before the submission deadline of the next piece of coursework. Formative feedback will be provided for the non-assessed coursework, as well being provided via the lab sessions through discussion with the course lecturers, TAs, and demonstrators.
|No Exam Information
On completion of this course, the student will be able to:
- Obtain experience in the design, implementation, training, and evaluation of machine learning systems
- Read technical papers, and explain their relevance to the chosen approach
- Design and carry out appropriate experiments, and explain the methodology involved
- Evaluate the resultant system
- Write a scholarly report, suitably structured and with supporting evidence
|Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, 2016, MIT Press|
Michael Nielsen, Neural Networks and Deep Learning, 2016. Online at http://neuralnetworksanddeeplearning.com
Christopher M Bishop, Neural Networks for Pattern Recognition, 1995, Clarendon Press.
|Graduate Attributes and Skills
|Additional Class Delivery Information
||This course is focused on the implementation and evaluation of machine learning systems, and is lab-based.
|Keywords||machine learning,deep learning,neural networks
|Course organiser||Dr Hakan Bilen
Tel: (0131 6)50 2717
|Course secretary||Mrs Helen Tweedale
Tel: (0131 6)50 3827