EP 57 | MIT 6.036: Foundations of ML & Linear Classifiers

<p>Welcome to MIT 6.036! We kick off our machine learning journey by tackling a deep philosophical paradox: how can we reliably predict the future using only data from the past? Join us as we dissect the &quot;problem of induction,&quot; explore the six core characteristics of ML problem classes, and jump into the elegant 2D geometry of Linear Classifiers to understand how algorithms draw boundaries.</p><p><strong>Key Topics:</strong></p><ul><li><p>The core differences between Machine Learning, Statistics, and Social Sciences.</p></li><li><p>The 6 Characteristics of ML Problem Classes (Supervised, Unsupervised, RL).</p></li><li><p>The &quot;Homework vs. Exam&quot; analogy for training error vs. generalization.</p></li><li><p>The mathematical geometry of a Separating Hyperplane and K-Fold Cross Validation.</p></li></ul><p><strong>Disclaimer:</strong> <em>Note: This is an AI-generated discussion created using Google&#39;s NotebookLM, Gemini, and other AI tools, based on the freely and publicly available MIT 6.036 - Introduction to Machine Learning course material and personal study notes.</em></p>