Machine learning aims to build computer systems that learn from experience. It is a fast-developing and interdisciplinary field, with historical roots in computer science, statistics, pattern recognition, neuroscience and physics. Many approaches in machine learning have led to rapid theoretical advances and real-world applications.This course attempts to bring together many of the important ideas and algorithms in machine learning, and explain them in a systematic and unified framework. It covers topics in regression, classification, mixture models, neural networks, deep learning, ensemble methods and reinforcement learning.