Machine learning is the science of getting computers to act without being\n explicitly programmed. In the past decade, machine learning has given us\n self-driving cars, practical speech recognition, effective web search,\n and a vastly improved understanding of the human genome. Machine learning\n is so pervasive today that you probably use it dozens of times a day without\n knowing it. Many researchers also think it is the best way to make progress\n towards human-level AI. In this class, you will learn about the most effective\n machine learning techniques, and gain practice implementing them and getting\n them to work for yourself. More importantly, you'll learn about not only\n the theoretical underpinnings of learning, but also gain the practical\n know-how needed to quickly and powerfully apply these techniques to new\n problems. Finally, you'll learn about some of Silicon Valley's best practices\n in innovation as it pertains to machine learning and AI.
\nThis course provides a broad introduction to machine learning, datamining,\n and statistical pattern recognition. Topics include: (i) Supervised learning\n (parametric/non-parametric algorithms, support vector machines, kernels,\n neural networks). (ii) Unsupervised learning (clustering, dimensionality\n reduction, recommender systems, deep learning). (iii) Best practices in\n machine learning (bias/variance theory; innovation process in machine learning\n and AI). The course will also draw from numerous case studies and applications,\n so that you'll also learn how to apply learning algorithms to building\n smart robots (perception, control), text understanding (web search, anti-spam),\n computer vision, medical informatics, audio, database mining, and other\n areas.
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\nThe course includes programming assignments and some programming background\n will be helpful.
\nNo, it is self-contained.
\nYes. Students who successfully complete the class will receive a statement\n of accomplishment signed by the instructor.
\n![](\"https://s3.amazonaws.com/coursera/media/Andrew_Ng.png\" "\\"Image:")
\nProfessor Andrew Ng\u00a0is 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.
\nIn 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.
\nNg 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.
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