Stanford CS229: Machine Learning – Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pqkTry
This lecture covers supervised learning and linear regression.
Andrew Ng
Adjunct Professor of Computer Science
https://www.andrewng.org/
To follow along with the course schedule and syllabus, visit:
http://cs229.stanford.edu/syllabus-autumn2018.html
#andrewng #machinelearning
Chapters:
00:00 Intro
00:45 Motivate Linear Regression
03:01 Supervised Learning
04:44 Designing a Learning Algorithm
08:27 Parameters of the learning algorithm
14:44 Linear Regression Algorithm
18:06 Gradient Descent
33:01 Gradient Descent Algorithm
42:34 Batch Gradient Descent
44:56 Stochastic Gradient Descent
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pqkTry
This lecture covers supervised learning and linear regression.
Andrew Ng
Adjunct Professor of Computer Science
https://www.andrewng.org/
To follow along with the course schedule and syllabus, visit:
http://cs229.stanford.edu/syllabus-autumn2018.html
#andrewng #machinelearning
Chapters:
00:00 Intro
00:45 Motivate Linear Regression
03:01 Supervised Learning
04:44 Designing a Learning Algorithm
08:27 Parameters of the learning algorithm
14:44 Linear Regression Algorithm
18:06 Gradient Descent
33:01 Gradient Descent Algorithm
42:34 Batch Gradient Descent
44:56 Stochastic Gradient Descent
