16. Learning: Support Vector Machines
1,955,760
Published 2014-01-10
View the complete course: ocw.mit.edu/6-034F10
Instructor: Patrick Winston
In this lecture, we explore support vector machines in some mathematical detail. We use Lagrange multipliers to maximize the width of the street given certain constraints. If needed, we transform vectors into another space, using a kernel function.
License: Creative Commons BY-NC-SA
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All Comments (21)
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Prof Patrick Winston has sadly passed away on July 19, 2019 rest in peace , the knowledge you’ve passed to thousand of students is your legacy and its forever thank you
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I’m jealous of every single student in this class. And thank god i am alive and can watch this on youtube.
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Tremendous respect for any professor who writes out the entire math on board and does not use notes to do so.
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I've been watching a lot of MIT, Stanford, Harvard, Princeton lectures, but this... This was phenomenal, hands down the best lecture I've ever seen. Rest in Peace Prof
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Feel blessed to have attended his lectures live and work under his supervision. Rest in peace Prof. You will always be an inspiration to me.
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I just came from Andrew Ng's ML course in order to understand SVMs better. I found something quite interesting. Andrew gets the optimization criterion at 21:49 from an altogether different place. He arrives at SVMs by modifying the logistic regression's cost function, and the optimization criterion emerges from the regularization portion of the cost function. He then explains why that leads to a maximum margin. In contrast, this professor starts by obtaining the margin width algebraically with the intention of maximizing it, and then explains why that leads to separating data. Pretty cool.
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RIP ! Prof Winston!!! you inspired lots of ppl!
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Machine learning is one of the worst taught classes in schools today - lecturers who are too into implementations and don't understand the basics well enough themselves, don't have motivation to teach well, and overcrowded classes because everyone wants to be a data scientist.. Thank you MIT for releasing this gem into the public domain for millions to watch.. This was easily one of the best SVM lectures ever!!
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MIT offering free courses on YouTube in the early moves is the ultimate education move. I respect it.
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This is why MIT is MIT. Good work Prof and Thank you to the team. We hope to see more lectures related to Machine learning and Data science from MIT.
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Professor if you ever read this, THANK YOU. I was actually sad the lecture ended eventually. The world needs more teaching like yours.
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Best explanation of SVM on internet !
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The historical part of Vapnik’s story is very inspiring.
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RIP Professor, the world needs more people like you.
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RIP Prof Patrick this lecture is gold , Never saw anyone explain all the tiny details this smooth in less than an hour
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This is how things should always be taught. Patience, deep understanding and passion to teach. I wish I would've had a professor like him as a graduate student.
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Simply loved it! Don't have any words for the professor who taught the sophisticated concepts with such simplicity...
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BRILLIANT. Massive respect for the knowledge and simplicity of the professor here.
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RIP Patrick!!!It is sad you are no longer with us. You are a great teacher..
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One of the best lectures I ever heard-methodical & extremely helpful!Thank you.I will definitely come back for more - appreciate this.
