Description
Machine learning is the science of getting computers to act without being
explicitly programmed. In the past decade, machine learning has given us
self-driving cars, practical speech recognition, effective web search,
and a vastly improved understanding of the human genome. Machine learning
is so pervasive today that you probably use it dozens of times a day without
knowing it. Many researchers also think it is the best way to make progress
towards human-level AI. In this class, you will learn about the most effective
machine learning techniques, and gain practice implementing them and getting
them to work for yourself. More importantly, you'll learn about not only
the theoretical underpinnings of learning, but also gain the practical
know-how needed to quickly and powerfully apply these techniques to new
problems. Finally, you'll learn about some of Silicon Valley's best practices
in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining,
and statistical pattern recognition. Topics include: (i) Supervised learning
(parametric/non-parametric algorithms, support vector machines, kernels,
neural networks). (ii) Unsupervised learning (clustering, dimensionality
reduction, recommender systems, deep learning). (iii) Best practices in
machine learning (bias/variance theory; innovation process in machine learning
and AI). The course will also draw from numerous case studies and applications,
so that you'll also learn how to apply learning algorithms to building
smart robots (perception, control), text understanding (web search, anti-spam),
computer vision, medical informatics, audio, database mining, and other
areas.