Postgraduate Course: Numerical Methods for Data (MATH11240)
|School||School of Mathematics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|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.
Recap: Matrix Computation
Topic 1: Networks
Topic 2: Inverse Problems
Topic 3: Deep Learning with Artificial Neural Networks
Topic 4: Adversarial Attacks
Entry Requirements (not applicable to Visiting Students)
|| Students MUST have passed:
Numerical Linear Algebra (MATH10098)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2023/24, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||Hours & Minutes
|Main Exam Diet S2 (April/May)||Numerical Methods for Data (MATH11240)||2:00|
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.
Linear Algebra and Learning from Data, G. Strang, Wellesley-Cambridge Press, 2019.
A First Course in Network Theory, E. Estrada and P. A. Knight, Oxford, 2015.
Discrete Inverse Problems: Insight and Algorithms, P. C. Hansen, SIAM, 2010.
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.
Intriguing properties of neural networks, C. Szegedy et al., Int Conf Learning Representations, 2014.
|Graduate Attributes and Skills
|Course organiser||Dr Kostas Zygalakis
Tel: (0131 6)50 5975
|Course secretary||Miss Gemma Aitchison
Tel: (0131 6)50 9268