Artificial Intelligence & Machine Learning 2 - Linear Regression | Stanford CS221: AI (Autumn 2021)

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