PPT-Quick Review Probability Theory

Mixture of Transparencies created by Dr Eick and Dr Russel Reasoning and Decision Making Under Uncertainty Quick Review Probability Theory Bayes Theorem and Naïve Bayesian Systems Bayesian Belief Networks. Through this class we will be relying on concepts from probability theory for deriving machine learning algorithms These notes attempt to cover the basics of probability theory at a level appropriate for CS 229 The mathematical theory of probability Continued Fractions. Lisa Lorentzen. Norwegian University of Science and Technology. Continued fraction:. Convergence:. Möbius. transformations:. Convergence:. Catch:. L (1986). General convergence:. CS648. . Lecture 3. Two fundamental problems. Balls into bins. Randomized Quick Sort. Random Variable and Expected . value. 1. Balls into BINS. Calculating probability of some interesting events. 2. Hedonism. . Key players and ideas?. B’s Theory of Motivation. W. hat . is it?. Moral Fact. What is it?. Initial . Ideas. Derivation. : How is the value or norm (idea of goodness which will come from it) derived?. tunity to of six a total of the of being a winner of the and "the last of the of other think that on each is essentially what we mean "heads on toss" are event occurred of the have no of the a poker i William W. Cohen. Machine Learning 10-605. Warmup. : Zeno’s paradox. Lance Armstrong and the tortoise have a race. Lance is 10x faster. Tortoise has a 1m head start at time 0. 0. 1. . So, when Lance gets to 1m the tortoise is at 1.1m. 1. Matt Gormley. Lecture 2. August 31, 2016. School of Computer Science. Readings:. Mitchell Ch. 1, 2, 6.1 – 6.3. Murphy Ch. 2. Bishop Ch. 1 - 2. 10-601 Introduction to Machine Learning. Reminders. Section 5.1. An event is the set of possible outcomes. Probability is between 0 and 1. The event A has a complement, the event not A. Together these two probabilities sum 1.. . ex. At least one and none are complements. Machine Learning. Chapter 1: Introduction. Example. Handwritten Digit Recognition. Polynomial Curve Fitting . Sum-of-Squares Error Function. 0. th. Order Polynomial. 1. st. Order Polynomial. 3. rd. William W. Cohen. Machine Learning 10-605. Jan 19 2012. Probabilistic and Bayesian Analytics. Andrew W. Moore. School of Computer Science. Carnegie Mellon University. www.cs.cmu.edu/~awm. awm@cs.cmu.edu. March 23, 2010. Outline. Intro & Definitions. Why learn about probabilities and risk?. What is learned?. Expected Utility. Prospect Theory. Scalar Utility Theory. Choices, choices, choices.... In the lab, reinforcement is often uniform. Slide . 2. Probability - Terminology. Events are the . number. of possible outcome of a phenomenon such as the roll of a die or a fillip of a coin.. “trials” are a coin flip or die roll. Slide . A bar is . obeying the law . when it has the following property:. If any of the patrons are below the age of 18, then that person is not drinking alcohol.. Legal or Illegal?. Patron. Age. Drink. Alice. and . RATIONALITY – Some general comments. 2. 3. Decision Theory. Formidable foundations. Probability and reasoning about the future. Rational decision making. Deeply rooted in the Enlightenment. Major leaps in the mid-20.