MOOCs and the Emerging Digital Classroom

Thu, 11 Apr 2013 07:07:14 GMT

Atmosphere, Ocean and Climate Dynamics - GFD0: Rotation stiffens fluids

Tue, 02 Apr 2013 07:08:49 GMT

Lecture 10: Continuous Bayes' Rule; Derived Distributions

Thu, 22 Nov 2012 08:05:06 GMT

Description: In this lecture, the professor discussed Bayes rule, Bayes variations, and derived distributions. Instructor: John Tsitsiklis

Lecture 12: Iterated Expectations; Sum of a Random Number of Random Variables

Thu, 22 Nov 2012 08:05:06 GMT

Description: In this lecture, the professor discussed conditional expectation and sum of a random number of random variables. Instructor: John Tsitsiklis

Lecture 19: Weak Law of Large Numbers

Thu, 22 Nov 2012 08:05:06 GMT

Description: In this lecture, the professor discussed limit theorems, Chebyshev's inequality, and convergence "in probability". Instructor: John Tsitsiklis

Lecture 9: Multiple Continuous Random Variables

Thu, 22 Nov 2012 08:05:06 GMT

Description: In this lecture, the professor discussed multiple random variables: conditioning and independence. Instructor: John Tsitsiklis

Lecture 23: Classical Statistical Inference I

Thu, 22 Nov 2012 08:05:05 GMT

Description: In this lecture, the professor discussed classical statistics, maximum likelihood (ML) estimation, and confidence intervals. Instructor: John Tsitsiklis

Lecture 24: Classical Inference II

Thu, 22 Nov 2012 08:05:05 GMT

Description: In this lecture, the professor discussed classical inference, Linear regression, and binary hypothesis testing. Instructor: John Tsitsiklis

Lecture 25: Classical Inference III; Course Overview

Thu, 22 Nov 2012 08:05:05 GMT

Description: In this lecture, the professor discussed classical inference, simple binary hypothesis testing, and composite hypotheses testing. Instructor: John Tsitsiklis

Lecture 2: Conditioning and Bayes' Rule

Thu, 22 Nov 2012 08:05:05 GMT

Description: In this lecture, the professor discussed conditional probability, multiplication rule, total probability theorem, and Bayes' rule. Instructor: John Tsitsiklis

Lecture 4: Counting

Thu, 22 Nov 2012 08:05:05 GMT

Description: In this lecture, the professor discussed principles of counting, permutations, combinations, partitions, and binomial probabilities. Instructor: John Tsitsiklis

Lecture 8: Continuous Random Variables

Thu, 22 Nov 2012 08:05:05 GMT

Description: In this lecture, the professor discussed probability density functions, cumulative distribution functions, and normal random variables. Instructor: John Tsitsiklis

Lecture 13: Bernoulli Process

Thu, 22 Nov 2012 08:05:04 GMT

Description: In this lecture, the professor discussed Bernoulli process, random processes, basic properties of Bernoulli process, distribution of interarrival times, the time of the kth success, merging and splitting. Instructor: John Tsitsiklis

Lecture 14: Poisson Process I

Thu, 22 Nov 2012 08:05:04 GMT

Description: In this lecture, the professor discussed Poisson process, distribution of number of arrivals, and distribution of interarrival times. Instructor: John Tsitsiklis

Lecture 16: Markov Chains I

Thu, 22 Nov 2012 08:05:04 GMT

Description: In this lecture, the professor discussed Markov process definition, n-step transition probabilities, and classification of states. Instructor: John Tsitsiklis

Lecture 21: Bayesian Statistical Inference I

Thu, 22 Nov 2012 08:05:04 GMT

Description: In this lecture, the professor discussed Bayesian statistical inference and inference models. Instructor: John Tsitsiklis Note: The first few minutes of this video are missing.

Lecture 22: Bayesian Statistical Inference II

Thu, 22 Nov 2012 08:05:04 GMT

Description: In this lecture, the professor discussed Bayesian statistical inference, least means squares, and linear LMS estimation. Instructor: John Tsitsiklis

Lecture 1: Probability Models and Axioms

Thu, 22 Nov 2012 08:05:03 GMT

Description: In this lecture, the professor discussed probability as a mathematical framework, probabilistic models, axioms of probability, and gave some simple examples. Instructor: John Tsitsiklis

Lecture 20: Central Limit Theorem

Thu, 22 Nov 2012 08:05:03 GMT

Description: In this lecture, the professor discussed central limit theorem, Normal approximation, 1/2 correction for binomial approximation, and De Moivre–Laplace central limit theorem. Instructor: John Tsitsiklis

Lecture 5: Discrete Random Variables; Probability Mass Functions; Expectations

Thu, 22 Nov 2012 08:05:03 GMT

Description: In this lecture, the professor discussed random variables, probability mass function, expectation, and variance. Instructor: John Tsitsiklis

Lecture 6: Discrete Random Variable Examples; Joint PMFs

Thu, 22 Nov 2012 08:05:03 GMT

Description: In this lecture, the professor discussed conditional PMF, geometric PMF, total expectation theorem, and joint PMF of two random variables. Instructor: John Tsitsiklis

Lecture 7: Multiple Discrete Random Variables: Expectations, Conditioning, Independence

Thu, 22 Nov 2012 08:05:03 GMT

Description: In this lecture, the professor discussed multiple random variables, expectations, and binomial distribution. Instructor: John Tsitsiklis

Lecture 11: Derived Distributions; Convolution; Covariance and Correlation

Thu, 22 Nov 2012 08:05:02 GMT

Description: In this lecture, the professor discussed derived distributions, convolution, covariance and correlation. Instructor: John Tsitsiklis

Lecture 15: Poisson Process II

Thu, 22 Nov 2012 08:05:02 GMT

Description: In this lecture, the professor discussed Poisson process, merging, splitting, and random incidence. Instructor: John Tsitsiklis

Lecture 17: Markov Chains II

Thu, 22 Nov 2012 08:05:02 GMT

Description: In this lecture, the professor discussed Markov process, steady-state behavior, and birth-death processes. Instructor: John Tsitsiklis

Lecture 18: Markov Chains III

Thu, 22 Nov 2012 08:05:02 GMT

Description: In this lecture, the professor discussed Markov Processes, probability of blocked phone calls, absorption probabilities, and calculating expected time to absorption. Instructor: John Tsitsiklis

Lecture 3: Independence

Thu, 22 Nov 2012 08:05:02 GMT

Description: In this lecture, the professor discussed independence of two events, independence of a collection of events, and independence vs. pairwise independence. Instructor: John Tsitsiklis

Meet 2011 TR35 Winner Brian Gerkey

Wed, 30 Nov 2011 05:00:00 GMT

Gerkey describes his work at EmTech 2011: A common language for robots Willow Garage PROBLEM: People who want to program robots have had to either write software from scratch or purchase proprietary software that is hard to modify. SOLUTION:

Recent Videos tagged 'Open access' on MIT Video

MOOCs and the Emerging Digital Classroom

Atmosphere, Ocean and Climate Dynamics - GFD0: Rotation stiffens fluids

Lecture 10: Continuous Bayes' Rule; Derived Distributions

Lecture 12: Iterated Expectations; Sum of a Random Number of Random Variables

Lecture 19: Weak Law of Large Numbers

Lecture 9: Multiple Continuous Random Variables

Lecture 23: Classical Statistical Inference I

Lecture 24: Classical Inference II

Lecture 25: Classical Inference III; Course Overview

Lecture 2: Conditioning and Bayes' Rule

Lecture 4: Counting

Lecture 8: Continuous Random Variables

Lecture 13: Bernoulli Process

Lecture 14: Poisson Process I

Lecture 16: Markov Chains I

Lecture 21: Bayesian Statistical Inference I

Lecture 22: Bayesian Statistical Inference II

Lecture 1: Probability Models and Axioms

Lecture 20: Central Limit Theorem

Lecture 5: Discrete Random Variables; Probability Mass Functions; Expectations

Lecture 6: Discrete Random Variable Examples; Joint PMFs

Lecture 7: Multiple Discrete Random Variables: Expectations, Conditioning, Independence

Lecture 11: Derived Distributions; Convolution; Covariance and Correlation

Lecture 15: Poisson Process II

Lecture 17: Markov Chains II

Lecture 18: Markov Chains III

Lecture 3: Independence

Meet 2011 TR35 Winner Brian Gerkey