Data Mining and Machine Learning I: Supervised and Unsupervised Learning (online) STATS5074
- Academic Session: 2018-19
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Summer
- Available to Visiting Students: No
- Available to Erasmus Students: No
- Taught Wholly by Distance Learning: Yes
This course introduces students to machine learning methods and modern data mining techniques, with an emphasis on practical issues and applications.
The course consists of short online lessons (each of at most 30 minutes length), totalling around 15-20 hours. Embedded in these lessons are formative quizzes and assessment tasks (not included in the above duration). These are flexible and can be taken (and re-taken) at any time. There also are 6-10 hours of tutorials and computer-based labs.
Requirements of Entry
The course is only available to students on the online MSc in Data Analytics.
Machine Learning (Level M)
100% Continuous Assessment
Project work is assessed by a report as well as an oral assessment and/or video presentation.
The aims of this course are:
■ to introduce students to different methods for dimension reduction and clustering (unsupervised learning);
■ to introduce students to a range of classification methods, beyond those covered in Predictive Modelling;
■ to introduce students to neural networks and deep learning;
■ to introduce students to kernel methods, support vector machines and Gaussian processes.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ apply and interpret methods of dimension reduction including principal component analysis, multidimensional scaling, the biplot and independent component analysis;
■ apply and interpret classical methods for cluster analysis;
■ apply and interpret advanced methods for classification;
■ fit neural networks, support vector machines and Gaussian processes to data and asses their predictive ability objectively.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.