Artificial Intelligence & Machine learning 3 - Linear Classification | Stanford CS221 (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-l... Assistant Professor Dorsa Sadigh Assistant Professor in the Computer Science Department & Electrical Engineering Department https://profiles.stanford.edu/dorsa-s... To follow along with the course schedule and syllabus, visit: https://stanford-cs221.github.io/autu... 0:00 Introduction 0:06 Machine learning: linear classification 0:14 Linear classification framework 2:43 An example linear classifier 6:26 Hypothesis class: which classifiers? 7:34 Loss function: how good is a classifier? 10:07 Score and margin 11:55 Zero-one loss rewritten 12:43 Optimization algorithm: how to compute best? 16:28 Digression: logistic regression 17:28 Gradient of the hinge loss 19:34 Hinge loss on training data 22:34 Gradient descent (hinge loss) in Python 26:16 Summary so far