Undergraduate Course: Machine Learning and Data Analysis (ELEE10033)
Course Outline
| School | School of Engineering |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Machine Learning (ML) is the study of algorithms that are able to learn from existing data, in order to make predictions on new data. ML is becoming a key part of modern engineering; it is essential that engineers know how to train and evaluate ML algorithms, understand how they work, and are able to analyse data and identify whether it is suitable for use with ML.
This course aims to provide engineering students with the skills to process and examine different forms of data and an understanding of how ML algorithms can use this data to solve problems. They will learn how to implement these algorithms in Python. The students will also gain an awareness of: when it is appropriate to use a particular algorithm (if any); best practices; the issues that can occur when sourcing data and deploying ML in the real world. |
| Course description |
This course takes a hands-on approach to machine learning and data analysis. It combines lectures with coding-focused workshops using Jupyter notebooks, where students will analyse data and explore machine learning methods in Python, supported by some theory-based questions. Each week includes a lecture introducing the material and a follow-up workshop to reinforce it in practice.
The provisional topics for each week are:
1. Introduction
2. Linear models for regression
3. Linear models for classification
4. Data visualisation
5. Model selection and evaluation
6. Machine learning in society
7. Decision trees and ensemble methods
8. Gaussian processes
9. Deep neural networks
10. Modern neural network architectures and transfer learning
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Information for Visiting Students
| Pre-requisites | Please see the "Other Requirements" box |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2026/27, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Supervised Practical/Workshop/Studio Hours 22,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
54 )
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| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Exam 100%
The exam is divided into two sections. Section A consists of short questions on core machine learning methods and calculations. Section B contains longer questions assessing both theoretical understanding and practical application, including a code-reading and interpretation question. Students will not be required to write code.
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| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- interpret and analyse code for data analysis and machine learning tasks;
- explain the principles and workings of common machine learning algorithms;
- perform calculations relevant to machine learning and data analysis;
- describe best practices for preprocessing data and for training and evaluating machine learning models;
- describe ethical issues that can arise when deploying machine learning algorithms in society.
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Reading List
Optional but very helpful:
- Raschka, Liu, and Mirjalili. Machine Learning with PyTorch and Scikit-Learn. Packt Publishing, 2022.
- Murphy. Probabilistic Machine Learning: An Introduction. MIT Press, 2022. (free online) |
Additional Information
| Graduate Attributes and Skills |
The following Accreditation of Higher Education Programmes (AHEP4) learning outcomes are assessed as follows:
Exam: M1 Science, mathematics and engineering principles; M2 Problem analysis; M3 Analytical tools and techniques; M8 Ethics
The following Accreditation of Higher Education Programmes (AHEP4) learning outcomes covered, but not assessed: M12 Practical and workshop skills; M13 Materials, equipment, technologies and processes
The following Skills for Success areas are covered: Critical thinking; Problem solving; Curiosity; Reflection; Data and Digital Literacy |
| Keywords | Machine Learning,Data Analysis,Python |
Contacts
| Course organiser | Dr Elliot Crowley
Tel:
Email: elliot.j.crowley@ed.ac.uk |
Course secretary | Ms Ilaria Monfroni
Tel:
Email: imonfron@ed.ac.uk |
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