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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 modern frameworks (e.g. Scikit-learn and PyTorch). 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. The main topics are listed below.

Interpreting Data: loading and reading data, understanding different modalities, datasets, computing statistics

Finding Patterns: data exploration, visualisation, dimensionality reduction, principal component analysis, K-means

Supervised Learning: the concepts of regression and classification, generalisation, learning regression models, regularisation, linear classifiers, decision trees

Deployment: evaluation, validation, neural networks, optimisation, applications, ethics
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Programming Skills for Engineers 2 (SCEE08014) OR Engineering Software 2 (ELEE08017) OR Numerical Methods and Computing 2 (CIVE08017) OR Computational Methods for Chemical Engineers 2 (CHEE08011) 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 Introductory Applied Machine Learning (INFR10069) OR Machine Learning (INFR10086) OR Machine Learning and Pattern Recognition (INFR11130)
Other requirements Students who have taken Introductory Applied Machine Learning (INFR10069) in a previous year are not permitted to take this course.
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  None
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) Written Exam %: 0
Practical Exam %: 0
Coursework %: 100
- 50% mini-tests
- 25% applications and ethics assignment
- 25% programming assignment
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 helpful:
- C. Bishop. Pattern Recognition and Machine Learning (Springer 2006, free online)
- D. Barber. Bayesian Reasoning and Machine Learning (CUP 2012, free online)
- I. Goodfellow et al. Deep Learning (MIT Press 2016, free online)
Additional Information
Graduate Attributes and Skills Not entered
KeywordsMachine Learning,Data Analysis,Python
Course organiserDr Elliot Crowley
Course secretaryMrs Megan Inch-Kellingray
Tel: (0131 6)51 7079
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