PPT-Topic models

Source Topic models David Blei MLSS 09 Topic modeling Motivation Discover topics from a corpus Model connections between topics Model the evolution of topics over time Image annotation. The ARMApq series is generated by 12 pt pt 12 qt 949 949 949 Thus is essentially the sum of an autoregression on past values of and a moving average o tt t white noise process Given together with starting values of the whole series Authors: Rosen-. Zvi. , Griffiths, . Steyvers. , Smyth . Venue: the 20th Conference on Uncertainty in Artificial Intelligence. Year: 2004. Presenter: Peter Wu. Date: Apr 7, 2015. Title: The Author-Topic Model for Authors and Documents. UWC Writing Workshop. Spring 2014. Thesis Statements. What do you already know about thesis statements?. Introduction to Thesis Statements . A thesis statement:. Tells the reader how you will interpret the significance of the subject matter under discussion.. 0. Pre-industrial economies in transition. Topics:. Advanced Organic Economies . A characterisation of pre-industrial economies . Demographic forces and economic development. Causes and effects of population change in pre-industrial economies. RIMC Research Capacity Enhancement Workshops Series : “Achieving Research Impact”. How to choose a research topic. Stuff to consider:. What are the pressing problems in your domain?. Reflect on your life experiences . part 1. Andrea Tagarelli. Univ. of Calabria, Italy. Statistical topic modeling. . (1/3). Key assumption: . text . data represented as a mixture . of . topics. , i.e., probability distributions . over . Jie Tang. *. , Limin Yao. #. , and Dewei Chen. *. *. Dept. of Computer Science and Technology. Tsinghua University. #. Dept. of Computer Science, University of Massachusetts Amherst. April, 2009. ?. What are the major topics in the returned docs?. Padhraic Smyth. Department of Computer Science. University of California, Irvine . . Progress Report. New deadline. In class, Thursday February 18. th. (not Tuesday). Outline. 3 to 5 pages maximum. 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. Models for. Count Data. Doctor Visits. Basic Model for Counts of Events. E.g., Visits to site, number of purchases, number of doctor visits. Regression approach. Quantitative outcome measured. Discrete variable, model probabilities. Choosing a Good Topic. What Happens After Brainstorming Topics?. Strategies for Brainstorming an EDD Topic. Brainstorming Review. What Do You Know?. What Do You Love?. Don’t You Hate It When . . .. Here are some examples that you may use: . Should school uniforms be enforced everywhere? . Should . men get paternity leave from work. ? . Are . we too dependent on computers. ?. . Should animals be used for research. 2. A Base-Language. To serve as an intermediate-level language for high-level languages. To serve as a machine language for parallel machines. . - J.B. Dennis. ~ Data Flow Graphs ~. CPEG421-2001-F-Topic-3-II. OO. L 2. 0. 12 KY. O. T. O. Briefing & Report. By: Masayuki . Kouno. . (D1) & . Kourosh. . Meshgi. . (D1). Kyoto University, Graduate School of Informatics, Department of Systems Science. Ishii Lab (Integrated System Biology).