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Course Date: 22 September 2014 to 01 December 2014 (10 weeks)
Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
Professor Andrew Ng is Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 20 professors and about 150 students/post docs. At Stanford, he teaches Machine Learning, which with a typical enrollment of 350 Stanford students, is among the most popular classes on campus. His research is primarily on machine learning, artificial intelligence, and robotics, and most universities doing robotics research now do so using a software platform (ROS) from his group.
In 2008, together with SCPD he started SEE (Stanford Engineering Everywhere), which was Stanford's first attempt at free, online distributed education. Since then, over 200,000 people have viewed his machine learning lectures on YouTube, and over 1,000,000 people have viewed his and other SEE classes' videos.
Ng is the author or co-author of over 100 published papers in machine learning, and his work in learning, robotics and computer vision has been featured in a series of press releases and reviews. In 2008, Ng was featured in Technology Review's TR35, a list of "35 remarkable innovators under the age of 35". In 2009, Ng also received the IJCAI Computers and Thought award, one of the highest honors in AI.
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
What is the format of the class?
The class will consist of lecture videos, which are broken into small
chunks, usually between eight and twelve minutes each. Some of these may
contain integrated quiz questions. There will also be standalone quizzes
that are not part of video lectures, and programming assignments.
How much programming background is needed for the course?
The course includes programming assignments and some programming background
will be helpful.
Do I need to buy a textbook for the course?
No, it is self-contained.
Will I get a statement of accomplishment after completing this class?
Yes. Students who successfully complete the class will receive a statement
of accomplishment signed by the instructor.