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, crossvalidation, evaluating/comparing models
 Classification (logistic regression, naive Bayes, decision trees/random forests)
 Regularisation/sparse regression (ridge and lasso)
 Unsupervised clustering (kmeans, hierarchal clustering)

Course Delivery Information

Academic year 2024/25, 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)  1:30  
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: sara.wade@ed.ac.uk 
Course secretary  Miss Gemma Aitchison
Tel: (0131 6)50 9268
Email: Gemma.Aitchison@ed.ac.uk 

