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DRPS : Course Catalogue : School of Engineering : Electronics

Undergraduate Course: Data Analysis and Machine Learning 4 (ELEE10031)

Course Outline
SchoolSchool of Engineering CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryData-driven solutions using machine learning are becoming increasingly prevalent in society. It is essential that the modern engineer has the tools to analyse and interpret data, and be able to apply machine learning methods where appropriate. They should also have an appreciation of the ethical issues that can arise when making decisions based on these methods.

This course aims to provide engineering students with the skills to process and examine different forms of data in Python, and an understanding of how machine learning methods can use this data to solve classification and regression problems. They will learn how to implement these methods in Python using Scikit-learn. The students will also gain an awareness of: when it is appropriate to use a particular method (if any); best practices; the ethical issues that can occur when sourcing data and deploying machine learning in the real world.
Course description This course takes a hands-on approach to performing data analysis and using machine learning. It combines traditional teaching with lab sessions using interactive Jupyter notebooks where students will develop and run Python code to analyse data and try out machine learning methods for themselves. Each week's teaching will consist of a lecture to introduce material and a follow-up lab session to put it into practice.

This year, the topics for each week are:

1. Introduction, data modalities, variable types
2. Summarising and visualising data
3. Preprocessing data, principal component analysis, clustering
4. Machine learning and ethics
5. Linear models for regression
6. Linear models for classification
7. Model selection and evaluation
8. Classification and regression trees, bagging and boosting
9. Gaussian processes
10. Deep neural networks
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Programming Skills for Engineers 2 (SCEE08014) OR Informatics 2 - Introduction to Algorithms and Data Structures (INFR08026) AND Sensor Networks and Data Analysis 2 (ELEE08021)
Prohibited Combinations Students MUST NOT also be taking Machine Learning (INFR10086) OR Applied Machine Learning (INFR11211) OR Machine Learning and Pattern Recognition (INFR11130)
Other requirements Students taking this course MUST be proficient in using Python. This course assumes background knowledge of linear algebra, multivariable calculus, and probability.

Students who have taken Introductory Applied Machine Learning (INFR10069) in a previous year are not permitted to take this course.
Information for Visiting Students
Pre-requisitesPlease see the "Other Requirements" box
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  62
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 10, Supervised Practical/Workshop/Studio Hours 30, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 58 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Tests (50%) - Open book tests taken in-person consisting of (i) short answer questions on theory; (ii) some programming exercises.

Coursework 1 (20%) - The student will record a short presentation performing a case study on a real-world application of machine learning.

Coursework 2 (30%) - The student will perform exploratory data analysis and machine learning on a given dataset and will report on their findings.
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. read in and analyse different types of data;
  2. visualise and summarise datasets in Python;
  3. apply machine learning algorithms to new data in Python;
  4. describe best practices for training and evaluating machine learning models, and be aware of common pitfalls;
  5. appreciate the ethical issues that can arise when deploying machine learning algorithms in society.
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 Not entered
KeywordsMachine Learning,Data Analysis,Python
Course organiserDr Elliot Crowley
Course secretaryMs Brunori Viola
Tel: (0131 6)50 5687
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