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. Statistical learning is where ML meets statistical/econometric theory, i.e., understanding the theory of learning from data, understanding ML methods and how to use them. Python is a widely-used language for coding in Big Data applications but also in business and industry more widely. |
| Course description |
This course provides an introduction to, and exploration of, the field of statistical learning, including how to apply, implement and assess ML methods in the Python programming environment. Specifically, 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.
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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:
- Understand key concepts of statistical learning, including supervised and unsupervised learning, and techniques such as regression, classification, shrinkage, tree-based methods, SVMs, and neural networks.
- Implement machine learning methods using Python, including supervised (regression, classification) and unsupervised (PCA, clustering) learning.
- Design machine learning pipelines, apply LASSO, Ridge, and tree-based methods (bagging, boosting, random forests) to solve real-world problems.
- Evaluate machine learning models, select appropriate methods, and assess their ethical and practical limitations.
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Reading List
Main textbook:
James-Witten-Hastie-Tibshirani-Taylor, An Introduction to Statistical Learning with Applications in Python (ISL), Springer 2023. The textbook has many applications that can be used directly in the labs and is ideal for a course like this.
Supplementary textbook:
Hastie-Tibshirani-Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (ESL), Springer 2009. More advanced in terms of theory. |
Additional Information
| Course URL |
www.sgpe.ac.uk |
| Graduate Attributes and Skills |
Not entered |
| Additional Class Delivery Information |
The course is split 50:50 between these two objectives. Objective #1 is covered in 3 x 3 hour lecture sessions. Objective #2 is covered in 3 x 3 hour computer labs. |
| Keywords | Not entered |
Contacts
| Course organiser | |
Course secretary | |
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