EP 59 | MIT 6.036: Feature Representation

<p>Welcome to Week 3 of MIT 6.036! What happens when a linear classifier encounters a problem that can&#39;t be solved with a straight line? You change the shape of the universe. This week, we explore the &quot;High-Dimensional Transformation Trick,&quot; tackle the infamous XOR problem, and learn why feature engineering—like transforming blood types into factored vectors—is the true secret weapon of machine learning engineers.<strong>Key Topics:</strong></p><ul><li><p>The XOR problem and the limitations of linear boundaries.</p></li><li><p>Transforming 1D data into 2D parabolas to make it linearly separable.</p></li><li><p>The Polynomial Basis and systematic feature expansion.</p></li><li><p>Discrete Encodings: Thermometer codes, Factored codes, and the &quot;CS Trap&quot; of Binary encoding.<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></li></ul>