VIDEO | MIT 6.036: Visualizing Linear Classifiers & ML Foundations

<p>See the math come to life in our first visual breakdown of MIT 6.036. We illustrate the fundamental architecture of machine learning models before dropping into a 2D coordinate plane to manually draw out a separating hyperplane, proving exactly how linear algorithms decide what is positive and what is negative.</p><p><strong>Key Topics:</strong></p><ul><li><p>Visual maps of Supervised, Unsupervised, and Reinforcement Learning pipelines.</p></li><li><p>Plotting feature vectors and the spatial geometry of decision boundaries.</p></li><li><p>Step-by-step matrix multiplication walkthrough for spatial coordinates.</p></li><li><p>Visualizing how K-Fold Cross Validation partitions datasets.</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>