Course co-ordinator(s): Dr Wei Wei (Edinburgh), Dr Abdul-Lateef Haji-Ali (Edinburgh).
Aims:
In this course, students will develop:
• An understanding of the fundamental concepts and techniques used in data mining and machine learning.
• An understanding of the mathematics underpinning data mining and machine learning.
• A critical awareness of the appropriateness of different data mining and machine learning techniques and the relationships between them.
• Familiarity with common applications of data mining and machine learning techniques
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Linked course(s): F21DL Data Mining and Machine Learning (Excluded).
Location: Edinburgh, Malaysia.
Semester: 1.
Syllabus:
Basic Concepts: classification, clustering, supervised and unsupervised learning.
Generative Models: probabilistic graphical models; cluster analysis (including k-means clustering, expectationmaximisation and mixture models); regression analysis.
Discriminative Learning: Instance-based learning and decision tree learning; artificial neural networks (perceptron, multilayer perceptron, back-propagation, deep learning architectures); maximum entropy models; support vector machines; ensemble methods (such as bagging and boosting).
SCQF Level: 11.
Credits: 15.