PPT-PROBABILISTIC REASONING

Heng Ji jihrpiedu 0405 0408 2016 Bayesian networks More commonly called graphical models A way to depict conditional independence relationships between random variables A compact specification of full joint . - Charles Sanders Peirce. Using Models of Reasoning. A Return to Logos. Reasoning from Specific Instances. Progressing from a number of particular facts to a general conclusion. .. This is also known as inductive reasoning.. By Lindsey Busker and Sydney Thomson. Lawrence Kohlberg . Born in Bronxville, New York on October 25, 1927. Attended high school at Phillips Academy and then served in the Merchant Marine at the end of World War II. Sources: D. Jensen. “Research Methods for Empirical Computer Science.”. . William M.K. . Trochim. . “Research Methods Knowledgebase”. More on Causality. What is causality?. What’s Important About Causality?. (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. It’s Logical. What is Logic?. Logic. – The science of correct reasoning.. Reasoning. . – The drawing of inferences or conclusions from known or assumed facts. .. There are two main types of reasoning:. Humans use their common sense all the time. what is it?. can we instill it in our AI programs?. if not, what are the consequences for AI?. We might think of common sense reasoning as the knowledge accumulated through experience that gives us the ability to. Task Force. Final Report. 15 September . 2016. Guiding Principle: . Educational Policy must balance access and opportunity to achieve equity.. Recommendation I. Define quantitative reasoning. Recommendation I. A professional development program to help teachers better understand the mathematical reasoning needed for ratio, proportion, and proportional reasoning.. To understand the importance and significance of ratio, proportion, and proportional reasoning in the middle grades mathematics curriculum.. Elizabeth Gordon,. . NBCT. MYP & IB Biology Instructor. Me…. & You…. Middle School? High School?. Biology? Chemistry? Physics? Life Science?. My Dad’s a Space Alien. I . have evidence that proves . Indranil Gupta. Associate Professor. Dept. of Computer Science, University of Illinois at Urbana-Champaign. Joint work with . Muntasir. . Raihan. . Rahman. , Lewis Tseng, Son Nguyen, . Nitin. . Vaidya. Inductive Reasoning . When you use a pattern to find the next term in a sequence you’re using . inductive reasoning.. The conclusion you’ve made about the next terms in the pattern are called a . Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . - Charles Sanders Peirce. On the Radar. Researching the Persuasive Speech Assignment. Due Wednesday on . WebCT. (by 11:59 p.m.). Exam Two. This Friday in Lecture. Study Guide on Course Website. Workshops for the Persuasive Speech. CS772A: Probabilistic Machine Learning. Piyush Rai. Course Logistics. Course Name: Probabilistic Machine Learning – . CS772A. 2 classes each week. Mon/. Thur. 18:00-19:30. Venue: KD-101. All material (readings etc) will be posted on course webpage (internal access).