Meet 2011 TR35 Winner Brian Gerkey
Meet 2011 TR35 Winner Brian Gerkey
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:
Lecture 9: Multiple Continuous Random Variables
Lecture 9: Multiple Continuous Random Variables
Description: In this lecture, the professor discussed multiple random variables: conditioning and independence. Instructor: John Tsitsiklis
Lecture 8: Continuous Random Variables
Lecture 8: Continuous Random Variables
Description: In this lecture, the professor discussed probability density functions, cumulative distribution functions, and normal random variables. Instructor: John Tsitsiklis
Lecture 7: Multiple Discrete Random Variables: ...
Lecture 7: Multiple Discrete Random Variables: Expectations, Conditioning, ...
Description: In this lecture, the professor discussed multiple random variables, expectations, and binomial distribution. Instructor: John Tsitsiklis
Lecture 6: Discrete Random Variable Examples; Joint PMFs
Lecture 6: Discrete Random Variable Examples; Joint PMFs
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 5: Discrete Random Variables; Probability Mass ...
Lecture 5: Discrete Random Variables; Probability Mass Functions; ...
Description: In this lecture, the professor discussed random variables, probability mass function, expectation, and variance. Instructor: John Tsitsiklis
Lecture 4: Counting
Lecture 4: Counting
Description: In this lecture, the professor discussed principles of counting, permutations, combinations, partitions, and binomial probabilities. Instructor: John Tsitsiklis
Lecture 3: Independence
Lecture 3: Independence
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
Lecture 2: Conditioning and Bayes' Rule
Lecture 2: Conditioning and Bayes' Rule
Description: In this lecture, the professor discussed conditional probability, multiplication rule, total probability theorem, and Bayes' rule. Instructor: John Tsitsiklis
Lecture 25: Classical Inference III; Course Overview
Lecture 25: Classical Inference III; Course Overview
Description: In this lecture, the professor discussed classical inference, simple binary hypothesis testing, and composite hypotheses testing. Instructor: John Tsitsiklis
Lecture 24: Classical Inference II
Lecture 24: Classical Inference II
Description: In this lecture, the professor discussed classical inference, Linear regression, and binary hypothesis testing. Instructor: John Tsitsiklis
Lecture 23: Classical Statistical Inference I
Lecture 23: Classical Statistical Inference I
Description: In this lecture, the professor discussed classical statistics, maximum likelihood (ML) estimation, and confidence intervals. Instructor: John Tsitsiklis
Lecture 22: Bayesian Statistical Inference II
Lecture 22: Bayesian Statistical Inference II
Description: In this lecture, the professor discussed Bayesian statistical inference, least means squares, and linear LMS estimation. Instructor: John Tsitsiklis
Lecture 21: Bayesian Statistical Inference I
Lecture 21: Bayesian Statistical Inference I
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 20: Central Limit Theorem
Lecture 20: Central Limit Theorem
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 1: Probability Models and Axioms
Lecture 1: Probability Models and Axioms
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 19: Weak Law of Large Numbers
Lecture 19: Weak Law of Large Numbers
Description: In this lecture, the professor discussed limit theorems, Chebyshev's inequality, and convergence "in probability". Instructor: John Tsitsiklis
Lecture 18: Markov Chains III
Lecture 18: Markov Chains III
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 17: Markov Chains II
Lecture 17: Markov Chains II
Description: In this lecture, the professor discussed Markov process, steady-state behavior, and birth-death processes. Instructor: John Tsitsiklis