Postgraduate Course: Machine Learning in Python (MATH11205)
|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
|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.
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)
Course Delivery Information
|Academic year 2023/24, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||Coursework will consist of at least two applied machine learning projects and weekly workshop assignments.
||Written feedback on both projects ¿ potentially marked with gradescope if available.
No written feedback on workshop assignments ¿ solutions posted after the deadline.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||Machine Learning in Python (MATH11205)||2:00|
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.
|- 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
|Graduate Attributes and Skills
|Course organiser||Dr Sara Wade
Tel: (0131 6)50 5085
|Course secretary||Miss Gemma Aitchison
Tel: (0131 6)50 9268