Course co-ordinator(s): Dr Ioannis Konstas (Edinburgh), Abdullah Almasri (Malaysia).
Aims:
To provide students with an in-depth introduction to data mining and machine learning and to the mathematics underpinning these techniques. Topics will include generative and discriminative approaches, classification, clustering, regression, and supervised and unsupervised learning. Students will develop practical experience by using the WEKA software to apply a range of machine learning algorithms to benchmark datasets.
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Location: Edinburgh, Malaysia.
Semester: 1.
Syllabus:
Basic Concepts: classification, clustering, regression, supervised and unsupervised learning.
Generative Models: probabilistic graphical models; cluster analysis (k-means, expectation-maximisation, mixture models, hierarchical models); regression analysis.
Discriminative Learning: Instance-based learning and decision tree learning; artificial neural networks (perceptron, MLPs, back propagation, introduction to deep learning architectures); maximum entropy models; support vector machines; ensemble learning (bagging, boosting, stacking, random forests).
Learning Outcomes: Subject Mastery
- An understanding of the fundamental concepts in data mining and machine learning.
- An understanding of the mathematics underpinning the data mining and machine learning techniques.
Learning Outcomes: Personal Abilities
- Rational problem identification and definition.
- Critical analysis and solution selection
Assessment Methods: Due to covid, assessment methods for Academic Year 2020-21 may vary from those noted on the official course descriptor. Please see the Computer Science Course Weightings and the Maths Course Weightings for 2020-21 Semester 1 assessment methods.
SCQF Level: 10.
Credits: 15.