Lecture 1 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.

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...

CS 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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  Education

[Doug Edmunds]

Write an algorithm to take the ums out, process his video, then repost it for us. Use his microphone1,2 output algo as a model.

[avatar098]

1:08:40 if you don't want to watch at all

[shyfocks]

Does anyone else find it ironic that stats have been disabled for this video?

[bataa UB]

coursera brought me here

[JesusSmurf]

All those "Uhm's" are wasting space on my Brain. Should I Defrag my self now ?

[Thomas Cameron]

Dam you for making me notice it. But I like his chiiled out style.

[ParadigmMMO]

This is all basic High School Stats

[James Lc]

This guy needs to take a lesson from sapolsky on how to present a lesson to a class.

He obviously have great information to share....but man hes soooooo soft and dull.

[Idol2011no]

We don't need to know how far they are, only how much closer one is to the other for each source. This can easily be observed in the time domain. You're obviously a clueless dumb fuck.

[Martin Jenkins]

"dealys"? I can't spell either, but in mitigation, I have had quite a lot of beer this evening. You might like to expand on "I do not accept that the delay to the sensors are unknowns". Given that a) it's part of the original problem definition and b) I removed that possibility in earlier challenges that you have yet to address. Because you can't do solve and can't admit that you're too dense to see that. If there's a "dumb fuck" in this thread, it's clearly you.

[Martin Jenkins]

Come on!

Q1:Three pairs of numbers. No delays. No transistors.

A1: You have no solution. What's keeping you?

Q2: Even simpler. One pair of numbers. Bounded range/domain.

A2: Also unsolved. What's keeping you?

It it because you are posting your "insights" on other subjects you know jack shit about? I see you getting shot down elsewhere too. Operetta? Purlease. My teachers don't understand me? Oh, stop it now. I'm crying.

Your "finely tuned bias" "theory" is so funny.

Solutions, you moron.

[Martin Jenkins]

You don't accept it because you, in your words, haven't got a fucking clue what you're talking about. THe dealys to the sensors is not unknown? So, how far away are they? I notice that after a couple of *days* you have failed to solve the kindergarten problem I set earlier. You might have been away. But, you posted in the thread and a second or two of anyone's time would be enough. Bias !- gain and neither compensate for the delay.

"Tree" transistors. You are an idiot and you can't even spell.