Summary of course cont​​ent

This course will introduce the principles of various fundamental machine learning techniques and their applications in data mining, computer vision specifically in the biomedical domain. This course covers various areas ranging from supervised learning to unsupervised learning as well as the various applications of machine learning that may be encountered in the biomedical industry.

​​Aims and objectives

Upon the successful completion of this course, students should be able to:

• Explain the motivations and rationale behind various machine learning algorithms.
• Apply machine learning techniques learnt to solve real-world biomedical problems.
• Acquire practical skills to process complex biomedical datasets.
• ​Explore some state-of-the-art machine learning techniques and apply them to domains related to biomedical applications.​

​​Syllabus

• Introduction to Machine Learning
• Supervised Learning
Bayesian Classifiers
Decision Tree
Artificial Neural Networks
Support Vector Machines (SVMs)
Regression Models
K-Nearest Neighbor Classifiers (KNN)
Ensemble Learning
• Feature Selection and Generation
• Unsupervised Learning
Clustering
Density Estimation
Dimension Reduction
• Deep Learning in Biomedical Applications

​​Assessment

Project Report	Individual	50%
Participation & Assignments	Individual	20%
Project Presentation	Group	30%
		100%