'Fair coin' presentation slideshows

Bayesian models of inductive learning

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CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Prof. Bart Selman selman@cs.cornell.edu Machine Learning: The Theory of Learning R&N 18.5. Machine Learning Theory. Central question in machine learning: How can we be sure that the hypothesis produced by a

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Chapter 4:

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2.1 Conditional Probability and Multiplication Rule

2.1 Conditional Probability and Multiplication Rule. Sometimes we are concerned with probabilities about some portion of the sample space.

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Bayesian models of human inference

Bayesian models of human inference. Josh Tenenbaum MIT. The Bayesian revolution in AI . Principled and effective solutions for inductive inference from ambiguous data: Vision Robotics Machine learning Expert systems / reasoning Natural language processing

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Human Cognition Reason without Rationality?

Human Cognition Reason without Rationality?. Hermit Crab. An arthropod in a mollusk shell. The shell evolved for a different organism than the crab carrying it down the beach.

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Probabilities and Proportions

Probabilities and Proportions. Lotto. I am offered two lotto cards: Card 1: has numbers Card 2: has numbers Which card should I take so that I have the greatest chance of winning lotto?. Roulette.

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Probability Review

Probability Review. 1A) A bag contains 4 white marbles, 6 black marbles, and 1 green marble. What is the probability of drawing a white marble?. A. C. B. D. 1B) A bag contains 4 white marbles, 6 black marbles, and 1 green marble. What is the probability of drawing a black marble?.

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A Brief Introduction to Information Theory

A Brief Introduction to Information Theory. 12/2/2004 ???. Outline. Motivation and Historical Notes Information Measure Implications and Limitations Classical and Beyond Conclusion. Motivation: What is information?.

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Simulation

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Jiaping Wang Department of Mathematical Science 02/04/2013, Monday

Chapter 4. Discrete Probability Distributions Section 4.1. Random Variables and Their Probability Distributions. Jiaping Wang Department of Mathematical Science 02/04/2013, Monday. Outline. Random Variables and Probability Functions Distribution Functions Examples.

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Randomness, Probability, and Simulation

Randomness, Probability, and Simulation. ACT Info. Investigating Randomness. Pretend that you are flipping a fair coin. Without actually flipping a coin, imagine the first toss. Write down the result you see in your mind, heads (H) or tails (T). Imagine a second flip. Write down the result.

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Random Variables

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5.1 Randomness

5.1 Randomness. The Language of Probability Thinking about Randomness The Uses of Probability. The Language of Probability. Chance behavior is unpredictable in the short run, but has a regular and predictable pattern in the long run.

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7.3 Binomial Trials

7.3 Binomial Trials. These type of experiments are called: Binomial Trials Bernoulli Trials The outcomes of a binomial trial are considered as: Success Failure . Since there are only two outcomes, we know that: Pr (success) + Pr (failure) = 1 If we call: Pr (success) = p

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Remaining course content Remote, fair coin flipping

DTTF/NB479: Dszquphsbqiz Day 33. Remaining course content Remote, fair coin flipping Presentations: Protocols, Elliptic curves, Info Theory, Quantum Crypto, Bitcoin , Error-correcting codes, Digital Cash Announcements: See schedule for weeks 9 and 10 Project workdays, exam

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Unit 11: Chi-Squared Tests

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Probability & Tree Diagrams

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