Postgraduate Course: Numerical Methods for Data (MATH11240)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | A data-fitting/approximation theory/numerical analysis view of modern computational techniques in data science and machine learning. Focuses on algorithms and the maths behind those algorithms. Complements other courses in the Schools of Mathematics and Informatics that focus on statistical viewpoints and/or implementation and testing. Draws on ideas from applied linear algebra and matrix computation. |
| Course description |
Recap: Matrix Computation
Topic 1: Networks
Topic 2: Inverse Problems
Topic 3: Deep Learning with Artificial Neural Networks
Topic 4: Adversarial Attacks
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Numerical Linear Algebra (MATH10098)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Supervised Practical/Workshop/Studio Hours 6,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
63 )
|
| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework: 50%
Exam: 50% |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Use spectral (eigenvector/eigenvalue) information in data analysis.
- Demonstrate understanding of the links between matrix computation (applied linear algebra) and network science (applied graph theory).
- Demonstrate understanding of mathematical concepts needed to design and train a deep learning network.
- Quantify success in a classification task.
- Apply numerical algorithms to problems in data science, including clustering problems, inverse problems and classification problems.
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Reading List
Illustrative Resources:
Matrix Computation:
Linear Algebra and Learning from Data, G. Strang, Wellesley-Cambridge Press, 2019.
Networks:
A First Course in Network Theory, E. Estrada and P. A. Knight, Oxford, 2015.
Inverse Problems:
Discrete Inverse Problems: Insight and Algorithms, P. C. Hansen, SIAM, 2010.
Deep Learning:
Deep learning: an introduction for applied mathematicians,C. F. Higham and D. J. Higham, SIAM Review, 860¿891, 2019.
Deep Learning, I. Goodfellow, Y. Bengio and A. Courville, MIT Press, 2016.
Adversarial Attacks:
Intriguing properties of neural networks, C. Szegedy et al., Int Conf Learning Representations, 2014. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | numerical,methods,data |
Contacts
| Course organiser | Dr Kostas Zygalakis
Tel: (0131 6)50 5975
Email: |
Course secretary | Miss Gemma Aitchison
Tel: (0131 6)50 9268
Email: |
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