Postgraduate Course: Financial Machine Learning II (Practical) (CMSE11528)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course focuses on the implementation and evaluation of machine learning systems in financial economics; specifically, it covers practical aspects of machine learning and focuses on practical and experimental issues in deep learning and neural networks. Students who take this course will obtain experience in the design, implementation, training, and evaluation of machine learning systems. |
| Course description |
Standard methods and theories in finance and economics are ill-equipped to capture complex data interactions presented in financial and related data. Deep learning approaches offer more useful insights into these complex big data interactions. This course, which covers practical aspects of machine learning and focuses on practical and experimental issues in the application of deep learning and neural networks to financial and economic data, will provide students with tools that are relevant to the big data challenges in financial economics.
-Application of Feed-forward network architectures to financial data
-Optimisation and learning rules
-Regularisation and normalisation
-Neural networks for classification
-Autoencoders
-Convolutional Neural Networks
-Recurrent Neural Networks
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Python Programming (MATH11199)
|
Co-requisites | Students MUST also take:
Introductory Applied Machine Learning (Semester 2) (INFR11205)
|
| Prohibited Combinations | |
Other requirements | A 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 and a basic understanding of probability and partial differentiation are is also necessary. Students should have programming experience. Programming in a numerical language will be required. |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
|
| Academic year 2022/23, Available to all students (SV1)
|
Quota: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
88 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% coursework (individual) - assesses all course Learning Outcomes |
| Feedback |
Formative: TBC
Summative: Individual feedback will be provided on the coursework assessment. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Develop critical experience in the design, implementation, training, and evaluation of machine learning systems
- Critically evaluate technical papers, and explain their relevance
- Design and carry out appropriate experiments, and explain the methodology involved
- Critically evaluate machine learning systems
- Write a scholarly report, suitably structured and with supporting evidence
|
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 |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Adam Ntakaris
Tel:
Email: |
Course secretary | Mrs Kelly-Ann De Wet
Tel: (0131 6)50 8071
Email: |
|
|
University Homepage