Master Linear Regression in AI & Machine Learning | Stanford CS221 Course

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai

Associate Professor Percy Liang
Associate Professor of Computer Science and Statistics (courtesy)
https://profiles.stanford.edu/percy-liang

Assistant Professor Dorsa Sadigh
Assistant Professor in the Computer Science Department & Electrical Engineering Department
https://profiles.stanford.edu/dorsa-sadigh

To follow along with the course schedule and syllabus, visit:
https://stanford-cs221.github.io/autumn2021/#schedule

0:00 Introduction
0:06 Machine learning: linear regression
0:10 The discovery of Ceres
0:55 Gauss's triumph
1:42 Linear regression framework
3:34 Hypothesis class: which predictors?
6:02 Loss function: how good is a predictor?
8:36 Loss function: visualization
9:23 Optimization algorithm: how to compute best?
11:17 Computing the gradient
13:24 Gradient descent example
15:24 Gradient descent in Python
17:06 Computing the cradient
21:21 Summary