Postgraduate Course: Machine Learning in Python (MATH11205)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Machine Learning techniques are of increasing importance in key applications in a variety of data driven problems. This course will seek to give a practical introduction to these techniques, backed up by using python to apply them to a variety of datasets. The course is suitable for students with some existing background in python, and basic knowledge of probability and statistics. |
| Course description |
This course is intended to provide an introduction to machine learning techniques. The course includes a discussion of some of the theory and ideas behind these techniques, as well as a chance to apply them in practice using a suitable toolkit available in python.
Topics may include:
- Introduction: supervised vs. unsupervised learning, regression vs. classification
- Linear regression: basis function expansion, overfitting
- Training, testing, generalisation, cross-validation, evaluating/comparing models
- Classification (logistic regression, naive Bayes, decision trees/random forests)
- Regularisation/sparse regression (ridge and lasso)
- Unsupervised clustering (k-means, hierarchal clustering)
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Course Delivery Information
|
| Academic year 2025/26, Not available to visiting students (SS1)
|
Quota: 80 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 14,
Supervised Practical/Workshop/Studio Hours 15,
Summative Assessment Hours 1.5,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
67 )
|
| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework will consist of at least two applied machine learning projects and weekly workshop assignments. |
| Feedback |
Written feedback on both projects - potentially marked with gradescope if available.
No written feedback on workshop assignments - solutions posted after the deadline. |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
|
| Main Exam Diet S2 (April/May) | Machine Learning in Python (MATH11205) | 90 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Explain the operation of different supervised and unsupervised algorithms and their practical uses.
- Select and apply a suitable learning algorithm to a range of basic problems.
- Use a suitable programming language to work with data and apply machine learning tools to it.
- Interpret the output and validity of a learning algorithm.
- Understand the derivations of supervised and unsupervised machine learning algorithms including their strengths, weaknesses, and conceptual explanations.
|
Reading List
- The Elements of Statistical Learning, Hastie et al. Springer (2001) ISBN 9781489905192
- Introduction to Machine Learning in Python, S.Guido & A.Muller O'Reilly (2016) ISBN 97814493369880
- Thoughtful Machine Learning with Python, M.Kirk O'Reilly (2017) ISBN 9781491924136 |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Machine Learning,Python,MLPy |
Contacts
| Course organiser | Dr Sara Wade
Tel: (0131 6)50 5085
Email: |
Course secretary | Miss Gemma Aitchison
Tel: (0131 6)50 9268
Email: |
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