Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics [...]

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, [...]

Generic Operators Despite the copyright notice on the screen, this course is now offered under a Creative Commons license: BY-NC-SA. Details at ocw.mit.edu

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on MDPs, value functions, and policy and value iteration. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning [...]

Lecture 14 of Clay Carson’s Introduction to African-American History Course (HIST 166) concentrating on the Modern Freedom Struggle (Fall 2007). This class session is a guest lecture by Elaine Brown on the Black Panther Party. Recorded November 13, 2007 at Stanford University. This course introduces the viewer to African-American history, with particular emphasis on the [...]

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include [...]

Lecture by Professor Jerry Cain for Programming Paradigms (CS107) in the Stanford University Computer Science department. In this lecture, Prof. Cain continues discussing semaphores, and moves on to more practical applications of threading in relation to C and C++ programming. Programming Paradigms (CS107) introduces several programming languages, including C, Assembly, C++, Concurrent Programming, Scheme, and [...]

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding’s inequalities. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement [...]

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen’s inequality, mixture of Gaussians, and expectation-maximization. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement [...]

Register Machines Despite the copyright notice on the screen, this course is now offered under a Creative Commons license: BY-NC-SA. Details at ocw.mit.edu