Lecture 2 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning.

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=A89DCFA6ADACE599

CCS 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

Category:

Education

Tags:

• science
• math
• engineering
• computer
• technology
• robotics
• algebra
• linear
• regression
• learning
• algorithm
• gradient
• descent
• normal
• equation

[caesiume]

I'm getting a Stanford education for free B) awesome

[kanobi14]

Awesome lectures.. I realise how good the Stanford and its professors are. Thanks a lot for believing in open education. May the force be with you.

[hylandsjgcn]

15:10



[rahulsanal1]

kindly check the link.. i can't view the video.. it says some error has occured for the past two days..

[wpeng001]

should add trace to x'x to make it equal to \sum X_{ii}

[ByThe1Way]

I know matrices But i was lost after he was talking about proving it.

[turkiym2]

@a1a2a3skurr1l The asker was clearly sleeping off his calculus classes.

[turkiym2]

@astroboomboy Mainly calculus and linear algebra, you may pick up the two in 2-3 months if you're intent on learning as they're usually freshman level courses and have no pre-requisites themselves (you may also learn them concurrently as they are independent on the basic level and will intertwine easily as necessary). For Calculus, I recommend the James Stewart textbook, as for linear algebra, I recommend the text by Otto Bretscher. Both are illustrative, thorough and easy to follow.

[harrycook111]

the calculus part is easy enough but he skipped most of the linear algebra details which is a big part of his math. So... you can go to MIT web site for another FREE education from Prof. Strang and the final equation will become very clear.

[shrit110]

watch after 9:00

[tessb]

@astroboomboy on the course website (google it) it says you need linear algebra and probability theory, but it said you need basic linear algebra and probability and a little programming experience.

[robertxxx74]

(see prev comment 1st) I often find these leaps in the math used by engineers to "prove" things work. I don't think it's just my lack of background either - he shows the operations he's using, then expects you to take it as read that the math really does work, and expects you to be able to follow right away. You don't usually need to understand the proofs to pass the course though, or to use the results. It's just not as satisfying.