Lecture 3 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic regression and how it relates 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

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

Category:

Education

Tags:

• science
• math
• engineering
• computer
• technology
• robotics
• algebra
• locally
• weighted
• logistic
• regression
• linear
• probabilistic
• interpretation
• Gaussian
• distribution
• digression
• perceptron

[david04268]

Thank you so much for sharing this great lecture!

[kunchichekkan]

If you can't understand the math, that is not the professor's problem.

[sonicblare]

At 32:How is that the 32:30, the professor says that the error is gaussian, then writes down on the other board saying , this implies that the probability of p( y/x;theta). I don't understand this implication. Can someone point me to a link or tell me how to get to the next step?

[wpeng001]

@JordanSoet because y is a m dimensional vector

[JordanSoet]

Does anyone know why when he changes to the likelihood definition from the probability, the y gets a little arrow on top of it?

[avico510]

wow he makes p values and error terms so complicated. the concepts are more important than the proofs for most

[harrycook111]

I think the question asked around 25min is really good. Really, the non-param method is like a purely interpolation method while param. method builds a model that helps extrapolate some output, whether correct or not depends on the system to be modeled.

[zhenjiezhang]

you need a good linear algebra text book.

[MultiYolgezer]

@astroboomboy

mit ocw classes single and multivariable calculus and linear algebra will suffice both have free lectures on youtube

[astroboomboy]

Anybody have any suggestions for me as to what kind of math I need to study in order to understand this? I have bought a book in calculus, but the book was very hard to learn from, but I will start putting in all my time come christmas. What kind of other math books should I get?

[thundercookies1981]

@alexplanation his point is that the interpretation of the function in that context shouldn't be construed as having anything to do with probability distributions. The function just happens to be a convenient one for LWR; as he said, you can use others, and some do.