Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)

ℹ️ EPISODE INFO

Released

April 17, 2020

Duration

1h 18m

Channel

Stanford Online

Watch on YouTube

▶ Open ↗

📝 EPISODE DESCRIPTION

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

🎤 SPEAKERS

• Andrew Ng

  host

  Computer scientist and AI researcher known for teaching Stanford CS229 and for leadership roles in machine learning industry and education.

🧾 EPISODE SUMMARY

In this episode of Stanford Online, featuring Andrew Ng, Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018) explores linear Regression, Gradient Descent Variants, and the Normal Equation Explained by Ng Ng motivates linear regression with a Portland housing price dataset, defining the hypothesis, parameters theta, training examples, and the notation used throughout the quarter.

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