Introduction to Optimization

Dive into the fundamentals of optimization in this 1-hour 12-minute tutorial led by Kevin Smith from MIT. Explore key concepts such as maximum likelihood estimation, cost functions, and gradient descent methods. Learn about convex and non-convex functions, local and global minima, and their implications in optimization problems. Discover practical applications through examples like balls in urns and coin flips. Advance to multi-dimensional gradients and their role in machine learning. Gain insights into stochastic gradient descent, regularization techniques, and sparse coding. Perfect for those looking to enhance their understanding of optimization principles and their applications in various fields, including machine learning.