Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 19 - principal components analysis

Stanford EE104: Introduction to Machine Learning Full Course - Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 19 - principal components analysis

Master Machine Learning: Stanford EE104 - Empower Your Future with 19 Rich Lectures on Key Concepts, from Predictors to Neural Networks and Unsupervised Learning!

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8 learners

What you'll learn

Understand the fundamentals of machine learning algorithms and their applications.
Develop skills to validate and evaluate machine learning models effectively.
Learn to apply empirical risk minimization techniques to various learning problems.
Gain proficiency in implementing and interpreting neural networks and classifiers.

This course includes

14 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

Sanjay Lall Stephen Boyd Engineering EE104 Stanford Machine Learning artificial intelligence AI supervised learning unsupervised learning regression overfitting

Full Transcript

Professor Sanjay Lall Electrical Engineering To follow along with the course schedule and syllabus, visit: http://ee104.stanford.edu To view all online courses and programs offered by Stanford, visit: https://online.stanford.edu/ 0:00 Introduction 0:12 Distance to a subspace 8:06 PCA data model 10:41 PCA loss function 13:19 PCA empirical risk 15:03 PCA empirical loss in matrix notation 16:35 Fitting a PCA model 18:26 Imputing with subspace data model 20:52 Approximate matrix factorization interpretation 27:36 PCA for embedding and dimension reduction 29:17 Approximate sometry property 32:25 Features from text 36:53 Example: Distinguishing texts 37:54 Example: 1000 characters of Kant 38:04 Example: 1000 characters of Russell 38:24 Example: Distinguishing tests

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