Postgraduate Course: Statistical Learning with Applications in Python (ECNM11099)
Course Outline
| School | School of Economics |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Machine learning (ML) methods are increasingly part of the applied econometrician's toolbox. Python is a widely-used language for coding in Big Data applications but also in business and industry more widely. This course would introduce students to the theory, concepts, methods, and Python coding training for understanding and using these methods. |
| Course description |
The course has two objectives:
1. An introduction to, and exploration of, the field of statistical learning, and specifically in terms of theory, concepts and methods.
2. How to implement these methods in Python.
Methods covered: regression and classification; resampling methods and cross-validation; shrinkage and dimension-reduction methods (lasso, ridge, PCA, etc.); tree-based methods (bagging, boosting, random forests, etc.); support vector machines; deep learning; model selection; ensemble learning; unsupervised learning (PCA, clustering).
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: 35 |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 18,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
80 )
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| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
50% exam, 50% Python group project |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Have a solid understanding of the theory behind ML methods and how/why they work.
- Understand when and how to use these methods in applied econometric problems.
- Be able to implement these solutions in Python.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Ina Taneva
Tel: (0131 6)51 5948
Email: |
Course secretary | Miss Quincy Sugiuchi
Tel: (0131 6)50 8361
Email: |
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