Machine Learning Algorithms — K-Means, Weighted Experts & Gradient Descent | Chapter 33 in Introduction to Algorithms
Machine Learning Algorithms — K-Means, Weighted Experts & Gradient Descent | Chapter 33 in Introduction to Algorithms Chapter 33 of Introduction to Algorithms introduces three foundational machine learning techniques: k-means clustering for unsupervised learning, multiplicative-weights algorithms for online decision-making, and gradient descent for optimization in large-scale data settings. These algorithms are essential tools across supervised, unsupervised, and online learning paradigms, and they serve as building blocks in modern data science and artificial intelligence. Watch the video for a full breakdown of how these algorithms work and where they apply. Subscribe to Last Minute Lecture for more data-driven summaries from classic textbooks. Types of Machine Learning The chapter begins by categorizing learning approaches: Supervised Learning: Learn from labeled data to predict outcomes (e.g., spam classification). Unsupervised Learning: Discover hidd...