Lecture 13 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression.

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
• learning
• algorithm
• expectation
• maximization
• EM
• mixture
• Gaussian
• naive
• Bayes
• model
• factor
• analysis
• digression
• distribution
• unsupervised

[sanfo0or]

if only he puts some problem examples to demonstrate the algorithms, it'll be better.

[MegaCrazyTaxiDriver]

I'm guessing the noise that we allow for each point, would make factor analysis more suitable in signal processing.

[mehdigoudarzi]

he's practically reading from those rusty papers ... reminds of some of my boring professors (sleeping pills).

[einsteinwallah2]

not good

[ictunsw2113]

what he is doing? Can't he solve without papers.

[kapildalwani]

EM algorithm, mixture of Gaussian and Mixture of Naives Bayes. Finally factor analysis.

[leonoel]

@sanfo0or Usually to exemplify those algorithms you need an extra class, and a huge number of calculations, try searching for some articles online