Course co-ordinator(s): Dr Ioannis Konstas (Edinburgh), Dr Dongdong Chen (Edinburgh), Dr Neamat El Gayar (Dubai), Radu-Casian Mihailescu (Dubai), Dr Abdullah Almasri (Malaysia).
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-requisite course(s): F29AI Artificial Intelligence and Intelligent Agents or basic knowledge of AI concepts and issues..
Location: Dubai, Edinburgh, Malaysia.
Semester: 1.
Syllabus:
Basic Concepts: datasets, dealing with missing data, classification, supervised vs unsupervised learning.
Generative Models: naïve Bayes, probabilistic graphical models, cluster analysis (such as k-means clustering, EM algorithm).
Discriminative Learning: linear regression, decision tree learning, perceptron, advanced models such as multi-layer perceptron and deep learning architectures.
SCQF Level: 10.
Credits: 15.