Undergraduate Course: Machine Learning Practical (INFR11132)
Course Outline
| 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 |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course is focused on the implementation and evaluation of machine learning systems, and is lab-based. Students who do this course will obtain experience in the design, implementation, training, and evaluation of machine learning systems.
Semester 1 comprises lectures, labs, and individual coursework.
Semester 2 is based around small group projects, and also includes tutorials and guest lectures.
Note: this course is not a stand-alone introduction to machine learning. Please see 'Other Requirements' for details. |
| Course description |
The course covers practical aspects of machine learning, and will focus on practical and experimental issues in deep learning and neural networks. Topics that are covered include:
* Feed-forward network architectures
* Optimisation and learning rules
* Regularisation and normalisation
* Neural networks for classification
* Autoencoders
* Convolutional Neural Networks
* Recurrent Neural Networks
MLP is coursework-based, with lectures to support the additional material required to carry out the practical. Students who complete this course will have experience in the design, implementation, training, and evaluation of machine learning systems.
MLP is a two-semester course. During Semester 1 the course will focus on developing a deep learning framework based on experiments using the task of classification of handwritten digits using the well-known MNIST dataset. The course uses a Python software framework, and a series of Jupyter notebooks. There is a series of ten weekly lectures in Semester 1 to provide the required theoretical support to the practical work.
Semester 2 will be based on small group projects, with a focus on using deep neural networks within the context of a miniproject, using an open source toolkit such as TensorFlow or PyTorch. Lectures in Semester 2 will support the coursework, and also provide insights to the current state of the art in this very fast moving area.
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Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Full Year |
| Course Start Date |
16/09/2024 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 12,
Supervised Practical/Workshop/Studio Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
164 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% Coursework:
Coursework 1 10%
Coursework 2 40%
Coursework 3 formative
Coursework 4 50% |
| Feedback |
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 |
Learning Outcomes
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
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Reading List
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. |
Additional Information
| Graduate Attributes and Skills |
Scientific communication
Report writing |
| 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 |
Contacts
| Course organiser | Dr Hakan Bilen
Tel: (0131 6)50 2717
Email: hbilen@inf.ed.ac.uk |
Course secretary | Miss Yesica Marco Azorin
Tel: (0131 6)50 5194
Email: ymarcoa@ed.ac.uk |
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