Pattern Recognition Machine Learning

AY2020/2021 Semester 2

This course gives an introduction to the fundamental concepts and methods in pattern recognition and machine learning. Topics covered include Bayesian decision theory, dimensionality reduction and feature selection, unsupervised learning and clustering, non-parametric techniques, support vector machines and kernel methods, neural networks and deep learning. Some applications of pattern recognition and machine learning are also included to help you appreciate the subject. Contents: Introduction to Pattern Recognition and Machine Learning. Bayesian Decision Theory and Maximum-Likelihood Estimation. Non-Parametric Techniques. Neural Networks and Deep Learning. Support Vector Machines and Kernel Methods. Unsupervised Learning and Clustering. Advanced Topics in Pattern Recognition and Machine Learning.