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 | 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 |
|
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
|
| Academic year 2015/16, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 8,
Supervised Practical/Workshop/Studio Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
80 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
No formal written examination; the assessment is based on two practical assignments and a written report submitted at the end of each |
| Feedback |
Summative feedback will provided through marking of, and comments on, the two assessed practicals. Detailed feedback from the first practical will be provided before the second practical deadline. All students will also be invited to sign-up to individual meetings to discuss the practical assessment. Formative feedback will provided via the lab sessions through discussion with the course lecturer and TA. |
| No Exam Information |
|
| Academic year 2015/16, Part-year visiting students only (VV1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 8,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
80 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
No formal written examination; the assessment is based on two practical assignments and a written report submitted at the end of each |
| Feedback |
Summative feedback will provided through marking of, and comments on, the two assessed practicals. Detailed feedback from the first practical will be provided before the second practical deadline. All students will also be invited to sign-up to individual meetings to discuss the practical assessment. Formative feedback will provided via the lab sessions through discussion with the course lecturer and TA. |
| No Exam Information |
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
|
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 | Ms Sarah Larios
Tel: (0131 6)51 4164
Email: |
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