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Title: Lecture 7 | Machine Learning (Stanford)
Added: Jul 22, 2008
Author: StanfordUniversity
Duration: 75:45
Description:
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.Complete Playlist for the Course:http://www.youtube.com/view_play_list?p=A89DCFA6ADACE599CS 229 Course Website:http://www.stanford.edu/class/cs229/Stanford University:http://www.stanford.edu/Stanford University Channel on YouTube:http://www.youtube.com/stanford
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Videos related to 'Lecture 7 | Machine Learning (Stanford)'
Channel: Education
Tags: science math engineering computer technology robotics learning algorithm optimal margin classifier prime dual optimization svm kernels convex kkt
science math engineering computer technology robotics learning algorithm optimal margin classifier prime dual optimization svm kernels convex kkt
Youtube Comments: 25
7errain Says:
Apr 26, 2009 - You have the most atrocious notation I've ever seen.. and I say this as someone who loves these lectures. Please talk to someone in math or physics. They've been refining notation for centuries now.
daywednes Says:
May 29, 2009 - it's very nice lecture. I really appreciate it
roywwcheng Says:
Jun 5, 2009 - Great lecture! Thanks for uploading..
1888junkteam Says:
Jan 13, 2010 - excellent work!
kourdoun85 Says:
Jan 13, 2010 - one sinlge question. @33:00. can someon tell me why THETA_p(w) = maxL(w,a,b) = f(w). thanks
halojetter Says:
Jan 19, 2010 - theta_p(w) = max L(w,a,b)=f(w) since some w satisfies the constraints, so h(w) will be zero and we can set alpha = 0 so as to maximize L(w,a,b)
knowledgekurd Says:
Apr 6, 2010 - Very good lectures, especially for statisticians
PatriciaHoffmanPhD Says:
May 19, 2010 - Soft Margin Support Vector Machines starts at time 38Lecture includes Karush-Kuhn-Tucker conditions
neverquitclimbing Says:
Jun 16, 2010 - GREAT series. I've been looking around for a *long* time for something to explain ML this clearly to me... Granted digging around gave me some necessary background - but still a definite jump in clarity over just reading Elements, etc...Thank you Stanford and Dr Ng!
timmurfy Says:
Jul 14, 2010 - this is phat
kapildalwani Says:
Sep 13, 2010 - his worst lecture so far. There is no sequence and he always say we will come back to it later and moves on. You must consult his lecture notes too!
albakrim Says:
Dec 14, 2010 - very good explanation of SVM thanks
2osiris1503 Says:
Aug 28, 2011 - now i know why should i learn math.
SkitchThat Says:
Nov 8, 2011 - Take his online courses, very interesting stuff.
grunder20 Says:
Nov 26, 2011 - fantastic lecture!!
grunder20 Says:
Dec 23, 2011 - Makes my head spin.
lovelplants Says:
Jan 29, 2012 - now that's clear. ty
rewtnode Says:
Feb 18, 2012 - Admitted, you can actually get these scribbles after reading the lecture notes. In fact that's the only way to be able to read the scribbles - pattern recognition ;-)
rewtnode Says:
Feb 18, 2012 - The lecture notes for this are very clear. read them and watch this lecture again: cs229.stanford.edu/notes/cs229-notes3.pdf
jumboo286 Says:
Mar 12, 2012 - Thank you so much, Prof.Andrew!
bizso09 Says:
Mar 24, 2012 - why no powerpoint and illustration?
bellagril5 Says:
Dec 19, 2008 - BORING