This course aims to provide students with a fundamental understanding of basic and emerging machine learning models and their applications in processing signals in various fields such as smart health, bioinformatics, adaptive control theory, medical image analysis, etc.
The course is designed so that the students can obtain the basic ideas and intuition behind modern machine learning methods as well as some formal understanding of how and why they work. Correspondingly, one set of topics will focus on the general theme of statistical inference, which will allow the students to apply the basic techniques to different types of data, such as sensor data, discrete samples from wearables, and medical images. Another set of topics will focus more on existing machine learning models/algorithms such as supervised learning (neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction); adaptive control; transfer learning. The course will also discuss recent applications of machine learning such as medical image analysis, multi-sensor data analysis, spatial and temporal signal processing. This course requires students to have prior knowledge on basic programming skills, at a level sufficient to write a reasonable computer program, basic probability theory and linear algebra.