Stanford EE104: Introduction to Machine Learning Full Course - Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 12 - classifiers

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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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 0:11 Categorical outputs 12:07 Applications 20:05 The two types of errors in Boolean classification 22:13 Boolean confusion matrix 26:45 Neyman Pearson metric 30:49 False positive and false negatives 33:54 ROC curve 36:17 Neyman-Pearson error 40:48 Example 48:05 Error types

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