Undergraduate Course: Machine Learning Practical (INFR11119)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | ***PLEASE NOTE - this course has been replaced by Machine Learning Practical INFR11132 (20 credit course) from 2016/17.***
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. |
| Course description |
The course will cover practical aspects of machine learning, and will focus on practical and experimental issues for a particular topic. Example topics on which the course could focus include:
* Artificial (deep) neural networks
* Reinforcement learning
* Gaussian processes
The course syllabus will thus be based on the particular focus for that year. For example, if the course focuses on artificial neural networks then following items would be covered:
* Feed-forward network architectures
* Optimisation of neural networks (stochastic gradient descent)
* Regularization
* Neural networks for classification
* Neural networks for regression
* Possible additional topics: Convolutional Neural Networks, Autoencoders, Restricted Boltzamnn Machines, Recurrent Neural Networks
MLP will be coursework-based, with 8 hours of lectures to support the additional material required to carry out the practical. Students who do this course will have experience in the design, implementation, training, and evaluation of machine learning systems.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Familiarity with basic mathematics, including algebra and calculus is essential. A reasonable knowledge of computational, logical, geometric and set-theoretic concepts is assumed. Working knowledge of vectors and matrices is also necessary. A basic grasp of probability and partial differentiation, is also required. Students should have programming experience. Programming in a numerical language (Python/Numpy) will be required: previous experience in Python is not mandatory. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- 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.
- 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
Bishop: Pattern Recognition and Machine Learning;
Murphy: Machine Learning - A Probabilistic Perspective.
Also specific material related to the course focus |
Contacts
| Course organiser | Prof Stephen Renals
Tel: (0131 6)50 4589
Email: |
Course secretary | Miss Claire Edminson
Tel: (0131 6)51 4164
Email: |
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