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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Machine Learning in Python (MATH11205)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryMachine 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)
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Python Programming (MATH11199) OR Computing and Numerics (MATH08065)
Prohibited Combinations Students MUST NOT also be taking Statistical Learning (MATH10094) OR Introductory Applied Machine Learning (INFR10069) OR Introductory Applied Machine Learning (INFD11005) OR Applied Machine Learning (INFR11211) OR Machine Learning and Pattern Recognition (INFR11130)
Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  None
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 Hours & Minutes
Main Exam Diet S2 (April/May)Machine Learning in Python (MATH11205)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Explain the operation of different supervised and unsupervised algorithms and their practical uses.
  2. Select and apply a suitable learning algorithm to a range of basic problems.
  3. Use a suitable programming language to work with data and apply machine learning tools to it.
  4. Interpret the output and validity of a learning algorithm.
  5. 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
KeywordsMachine Learning,Python,MLPy
Course organiserDr Sara Wade
Tel: (0131 6)50 5085
Course secretaryMiss Gemma Aitchison
Tel: (0131 6)50 9268
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