Empirical Risk Minimization Explained | The Engine Behind Modern AI скачать в хорошем качестве

Empirical Risk Minimization Explained | The Engine Behind Modern AI

What drives most modern machine learning algorithms? In this video, we break down Empirical Risk Minimization (ERM) — the core design principle behind many supervised learning methods. You’ll learn what ERM is, why it’s important, and how it forms the foundation of models like linear regression, neural networks, and support vector machines. 📘 Further Reading: Dive deeper into ERM in Chapter 4 of my open-access textbook: Alexander Jung, Machine Learning: The Basics, Springer, 2022 📖 Preprint PDF (Free) https://github.com/alexjungaalto/Mach... ⏱️ Timestamps: 00:00 Introduction 03:54 The Ultimate Goal of ML 04:32 Loss Functions 05:25 The i.i.d. Assumption 08:20 Risk or Expected Loss 10:00 The Law of Large Numbers 👨‍🏫 About the Channel: I'm Alexander Jung, Associate Professor at Aalto University. I create videos to make machine learning fundamentals accessible and intuitive for everyone. #machinelearning #EmpiricalRiskMinimization #ai #supervisedlearning #mlbasics #AlexanderJung