PPT-5. Vector Space and Probabilistic Retrieval Models

Many slides in this section are adapted from Prof Joydeep Ghosh UT ECE who in turn adapted them from Prof Dik Lee Univ of Science and Tech Hong Kong 1 These notes are based in part on notes by Dr Raymond J Mooney at the University of Texas at Austin . . Natarajan. Introduction to Probabilistic Logical Models. Slides based on tutorials by . Kristian. . Kersting. , James . Cussens. , . Lise. . Getoor. . & Pedro . Domingos. Take-Away Message . Chris Manning, Pandu Nayak and . Prabhakar. . Raghavan. Who are these people?. Stephen Robertson. Keith van . Rijsbergen. Karen . Sp. ä. rck. . Jones. Summary – vector space ranking. Represent the query as a weighted tf-idf vector. (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. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. Daniel Svozil. based on excelent video lectures by Gilbert Strang, MIT. http://ocw.mit.edu/OcwWeb/Mathematics/18-06Spring-2005/VideoLectures/index.htm. Lectur. e. 5, Lecture 6. Transposes. How to write tra. . Models. . 1. Overview. . Probabilistic Approach to Retrieval. . Basic Probability Theory. Binary Independence . Model. Bayesian Model. 2. Outline. . Probabilistic Approach to Retrieval. . Basic Probability Theory. Machine Learning @ CU. Intro courses. CSCI 5622: Machine Learning. CSCI 5352: Network Analysis and Modeling. CSCI 7222: Probabilistic Models. Other courses. cs.colorado.edu/~mozer/Teaching/Machine_Learning_Courses. Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. Textbook by. Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schutze. Notes Revised by X. . Meng. for SEU. May 2014. Retrieval Models and Vector Space Model. Retrieval Models. A retrieval model specifies the details of:. 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 . 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 . BY. DR. ADNAN ABID. Lecture # . Introduction. Library Management System. Structured Data Storage / Tables. Semi-Structured and Unstructured . Employee Department Salary. Library Digitization. Information Retrieval Models. computing the similarity between words. “. fast. ” is similar to “. rapid. ”. “. tall. ” is similar to “. height. ”. Question answering:. Q. : “. How . tall. . is Mt. Everest?”. Candidate A: “The . 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).