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

Strategic UX for Drupal Projects

Fri, 27 Jul 2012 07:03:50 GMT

Design 4 Drupal Camp Boston 2012: Strategic UX for Drupal Projects presented by Dani Nordin

Add Usability Testing To Your Skill Set

Thu, 26 Jul 2012 07:03:50 GMT

Design 4 Drupal Camp Boston 2012: Add Usability Testing To Your Skill Set presented by Dharmesh Mistry

Code Without Thinking

Thu, 26 Jul 2012 07:03:50 GMT

Design 4 Drupal Camp Boston 2012: Code Without Thinking presented by David Moore

Designing & Implementing Beautiful, Flexible Interfaces

Thu, 26 Jul 2012 07:03:50 GMT

Design 4 Drupal Camp Boston 2012: Designing & Implementing Beautiful, Flexible Interfaces presented by Ken Woodworth

Creating Fireworks Templates For Drupal

Wed, 25 Jul 2012 20:30:10 GMT

Design 4 Drupal Camp Boston 2012: Creating Fireworks Templates For Drupal presented by Dani Nordin

Display Suite

Wed, 25 Jul 2012 20:30:10 GMT

Design 4 Drupal Camp Boston 2012: Display Suite

Improving the Content Editing Experience in Drupal with Spark

Wed, 25 Jul 2012 20:30:10 GMT

Design 4 Drupal Camp Boston 2012: Improving the Content Editing Experience in Drupal with Spark

jQuery Demystified

Wed, 25 Jul 2012 20:30:10 GMT

Design 4 Drupal Camp Boston 2012: jQuery Demystified presented by Patrick Corbett

Keynote Address:Angie Byron

Wed, 25 Jul 2012 20:30:10 GMT

Design 4 Drupal Camp 2012: Keynote Address-Angie Byron

Opening

Wed, 25 Jul 2012 20:30:10 GMT

Design 4 Drupal Camp Boston 2012: Opening

Scalable Stylesheets- Theming with Modular CSS

Wed, 25 Jul 2012 20:30:10 GMT

Scalable Stylesheets- Theming with Modular CSS presented by John Ferris

EdX: The Future of Online Education is Now

Thu, 03 May 2012 20:19:22 GMT

EdX is a not-for-profit joint venture between Harvard University and the Massachusetts Institute of Technology to offer online versions of their classes and those of other universities.

Press conference: MIT, Harvard announce edX

Thu, 03 May 2012 14:30:13 GMT

MIT President Susan Hockfield and Harvard University President Drew Faust, accompanied by top officials from both institutions, announced on Wednesday a new collaboration that will unite the Cambridge-based universities in an ambitious new partnership to deliver online education to learners anywhere in the world.

The new venture, called edX, will provide interactive classes from both Harvard and MIT -- for free -- to anyone in the world with an Internet connection. But a key goal of the project, Faust said, is "to enhance the educational experience of students who study in our classrooms and laboratories."

Read the initial announcement -- http://web.mit.edu/newsoffice/2012/mit-harvard-edx-announcement-050212.html

FAQ: What is edX? -- http://web.mit.edu/newsoffice/2012/edx-faq-050212.html

Read the event coverage -- http://web.mit.edu/newsoffice/2012/edx-launched-0502.html

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:

Bridging the Gap: Mixing approaches, content and tools to help college students

Mon, 14 Nov 2011 21:03:33 GMT

The Next Generation Learning Challenge has provided a call to action for those involved in Open Educational Resources to meet the needs of the US education system. One of the challenges is to deploy open core courseware to address the retention and completion issues in community colleges. In the Open Learning: Bridge to Success (B2S) initiative The Open University working in partnership with MIT, Anne Arundel Community College (AACC) and University of Maryland University College (UMUC) will develop open bridging modules to help learners having problems in coping with credit bearing courses. Modules focussed on learning to learn and understanding mathematics will be released as complete open educational resource packages from The Open University's existing successful programme of entry-level (pre-degree) "Openings" modules. The Open University has an established open presence through its OpenLearn open content site which offers a wide range of units, and the courses will be developed in the open to benefit not only students in the partner institutions but any learners who wish to use them.

The project will run its first pilots with Community College students from September and this presentation focuses on the early phase of the project including: release of the initial materials, augmentation with other OER, design of the research methodology and early lessons from working together as partners. Already working in the open is changing how we think about the provision of content and the instruction of practical experiences alongside meeting curriculum needs. We anticipate presenting the design requirements and how they have been met through open provision, reflections from those involved in the projects, the first feedback from students at the pilot colleges, and the indications from the additional users in the open.

Educational Collaboration Space (ECS) at MIT

Wed, 22 Jun 2011 15:43:14 GMT

MIT math instructors have developed a tool to help educators get to 'best practices' and preserve them for posterity.

Session III: Panel

Mon, 23 Nov 2009 15:36:01 GMT

Discussion with Session III presenters.
Moderator: Leonidas Kontothanassis, Senior Staff Engineer, Performance Analysis, Google Boston Labs. 10/12/2009

Open Networks and Open Society: The Relationship between Freedom, Law, and Technology

Tue, 26 Apr 2005 04:00:00 GMT

Hal Abelson, PhD '73, Class of 1922 Professor of Computer Science and Engineering, MIT School of Engineering; John Wilbanks, Vice President, Science Commons Hal Abelson wants to deliver a one-two punch against the privatization of academic discourse. His weapons of choice? New global initiatives based on MIT's own OpenCourseWare (OCW) and DSpace. Abelson owns to a "real anxiety that people are quick to talk about academic exchange under the rubrics of property and ownership," along the lines of the motion picture, recording and publishing industries. He sees a profound threat -- that of eventual monopoly control -- to scholarly publishing. Out of self-protection, Abelson says, universities must pursue initiatives to ensure free and open academic publishing. Two coordinated initiatives would "strengthen the information commons," the body of knowledge on which thinkers continually build and which "forms the progress of science." One, modeled on OCW, would provide "global access to raw material from which the world's great learning institutions create educational experiences for their students." The other, like MIT Libraries' DSpace, would produce an interoperable and virtual collection of research from the world's top institutions. Abelson exhorts universities to pursue their true mission of generating, disseminating and preserving knowledge, and defend against the encroachments of the commercial publishing industry, with its near stranglehold on journals and increasingly on ideas themselves. John Wilbanks hopes to expand on this vision with Creative Commons, an attempt to permit authors and artists around the world to copyright their material with "some rights reserved." His website provides free tools for licensing music, photos, video or written works, while permitting the dissemination of this work for noncommercial or shared use, for instance. Eventually, Creative Commons may encompass data and datasets, as well as patents and the transfer of biological material. About the Speaker(s): Harold (Hal) Abelson is Class of 1922 Professor of Electrical Engineering and Computer Science at MIT and a Fellow of the IEEE. He holds an A.B. degree from Princeton University and a Ph.D. degree in mathematics from MIT. In 1992, Abelson was designated as one of MIT's six inaugural MacVicar Faculty Fellows, in recognition of his significant and sustained contributions to teaching and undergraduate education. Abelson was recipient in 1992 of the Bose Award (MIT's School of Engineering teaching award) and is also the winner of the 1995 Taylor L. Booth Education Award given by IEEE Computer Society, cited for his continued contributions to the pedagogy and teaching of introductory computer science. He is co-director of the MIT-Microsoft Research Alliance in educational technology, and co-head of the MIT Council on Educational Technology. Prior to Creative Commons, John Wilbanks held a Fellowship at the World Wide Web Consortium in Semantic Web for Life Sciences. Previously, he founded the bioinformatics company, Incellico, which built semantic graph networks for use in pharmaceutical discovery. Before founding Incellico, John was the first Assistant Director at the Berkman Center for Internet and Society at Harvard Law School and also spent time in Washington, DC, USA as a legislative aide to U.S. Representative Fortney ("Pete") Stark. John holds a B.A. in Philosophy from Tulane University. Host(s): Office of the Provost, Program on Human Rights and Justice Event date: 04/26/2005

Recent Videos tagged 'Open source' on MIT Video

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

Strategic UX for Drupal Projects

Add Usability Testing To Your Skill Set

Code Without Thinking

Designing & Implementing Beautiful, Flexible Interfaces

Creating Fireworks Templates For Drupal

Display Suite

Improving the Content Editing Experience in Drupal with Spark

jQuery Demystified

Keynote Address:Angie Byron

Opening

Scalable Stylesheets- Theming with Modular CSS

EdX: The Future of Online Education is Now

Press conference: MIT, Harvard announce edX

Meet 2011 TR35 Winner Brian Gerkey

Bridging the Gap: Mixing approaches, content and tools to help college students

Educational Collaboration Space (ECS) at MIT

Session III: Panel

Open Networks and Open Society: The Relationship between Freedom, Law, and Technology