Lecture 8 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels.

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
• support
• vector
• machine
• SVM
• kernel
• soft
• margin
• optimization
• SMO

[PatriciaHoffmanPhD]

Continues discussion of Soft Margin Support Vector Machine

Coordinate Assent Algorithm starts at 46

[horiac]

A good PDF of this lesson: goo*gl/3Jcc3

replace * with .

[lovelplants]

cool.. that's nice



[kmmbvnr]

@bloom987654322 too late, but i just want to check my conjecture: Is phi(x) for Gaussian just Taylor series for exponent?

[bloom987654322]

Does anyone know how to findthe phi(x) corresponding to Gaussian kernel? This is needed if we want to calculate w after solving the soft margin optimization problem.

[timmurfy]

this is phat

[olivermitevski]

1:13:00 I need to come back to it.