PPT-Probabilistic Automaton

Ashish Srivastava Harshil Pathak Introduction to Probabilistic Automaton Deterministic Probabilistic Finite Automata Probabilistic Finite Automaton Probably Approximately Correct PAC learnability. Simulations and Composition. Lecture . 05. Sayan. . Mitra. Plan for Today. Abstraction and Implementation relations (continued). Composition. Substitutivity. Looking ahead. Tools: PVS, . SpaceEx. , Z3, UPPAAL. and. Happy and fruitful New Year. שנה טובה. (Happy New Year). Staff. Instructor:. Amos Israeli room 2-106 .. Office Hours: . Fri 10-12 or by arrangement . Tel#: . 53. 4-886. e-mail:. aisraeli “at” cs.ucsd.edu. Fall 2013. Dr. Eric Rozier. Propositional Temporal Logic. Does the following hold?. yes. Propositional Temporal Logic. Does the following hold?. no. Examples: What do they mean?. . G F. . p. p holds infinitely often. A . shorted version from. :. Anastasia . Berdnikova. &. Denis . Miretskiy. ‘Colourless green ideas sleep furiously’.. Chomsky constructed finite formal machines – ‘. grammars. ’.. ‘Does the language contain this sentence?’ (intractable) . Anton von . Schantz. , Harri . Ehtamo. a. nton.von.schantz@aalto.fi. , . harri.ehtamo@aalto.fi. . The document can be stored and made available to the public on the open internet pages of Aalto University. All other rights are reserved.. Bottom-up parsing. Bottom-up parsing builds a parse tree from the leaves (terminals) to the start symbol. int. E. int. T. *. T. E. +. T. int. (4). (2). (3). (5). (1). int. *. +. int. E . . T + E | T. How the Quest for the Ultimate Learning Machine Will Remake Our World. Pedro Domingos. University of Washington. Machine Learning. Traditional Programming. Machine Learning. Computer. Data. Algorithm. Lexers. Example in . javacc. TOKEN. : {. <IDENTIFIER: <LETTER> (<LETTER> | <DIGIT> | "_")* >. | . <INTLITERAL: . <DIGIT> (<DIGIT>)* . >. | <LETTER: ["a"-"z"] | ["A"-"Z"]>. Automata, Logic and infinite games”, edited by . Gradel. , Thomas and Wilke. Games, logic and Automata Seminar. Assaf. Ben Shimon. ω. -. Automata. Intro. The main topic covered in this chapter is the question how to define acceptance of infinite words by finite automata.. Systems. Timed to Hybrid Automata . Sayan. . Mitra. . (edited by Yu Wang). Lecture 10. Announcements. HW2 released . start soon. Readings. [Henz95]. Thomas . Henzinger. , Peter . Kopke. , . Anuj. . 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 . Regular Languages Refresher. Roman . Manevich. Ben-Gurion University of the Negev. Regular languages refresher. 2. Regular languages refresher. Formal languages. Alphabet = finite set of letters. Word = sequence of letter. Chapter 5: Probabilistic Query Answering (3). 2. Objectives. In this chapter, you will:. Learn the definition and query processing techniques of a probabilistic query type. Probabilistic Reverse Nearest Neighbor Query. 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).