PPT-CSE 446: Expectation Maximization (EM)

Winter 2012 Daniel Weld Slides adapted from Carlos Guestrin Dan Klein amp Luke Zettlemoyer Machine Learning 2 Supervised Learning Parametric Reinforcement Learning Unsupervised Learning. AN TA YL OR GUCC PO LO RALP LA UREN LA OST MICHAEL KO RS 36 37 KI O 37 37 36 36 36 35 34 34 34 34 33 33 33 32 32 30 30 30 29 29 28 28 27 27 27 26 26 26 262 GH B AS CO BA TH BOD Y WO RK BLDG 13 BLDG 31 REEBOK GAP OUTL ET BLDG 1 21 NE BA LANC HF BLD Rao CSE 326 CSE 326 Lecture 7 More on Search Trees Todays Topics Lazy Operations Run Time Analysis of Binary Search Tree Operations Balanced Search Trees AVL Trees and Rotations Covered in Chapter 4 of the text R Hongning Wang. CS@UVa. Today’s lecture. k. -means clustering . A typical . partitional. . clustering . algorithm. Convergence property. Expectation Maximization algorithm. Gaussian mixture model. . Honglei. . Zhuang. , . Yihan. Sun, Jie Tang, Jialin Zhang, Xiaoming Sun. Influence Maximization. 0.6. 0.5. 0.1. 0.4. 0.6. 0.1. 0.8. 0.1. A. B. C. D. E. F. Probability . of . influence. Marketer Alice. Machine Learning. Last Time. Expectation Maximization. Gaussian Mixture Models. Today. EM Proof. Jensen’s Inequality. Clustering sequential data. EM over . HMMs. EM in any Graphical Model. Gibbs Sampling. Mixture Models and Expectation Maximization. Machine Learning. Last Time. Review of Supervised Learning. Clustering. K-means. Soft K-means. Today. Gaussian Mixture Models. Expectation Maximization. The Problem. Machine . Learning . 10-601. , Fall . 2014. Bhavana. . Dalvi. Mishra. PhD student LTI, CMU. Slides are based . on materials . from . Prof. . Eric Xing, Prof. . . William Cohen and Prof. Andrew Ng. Honglei. . Zhuang. , . Yihan. Sun, Jie Tang, Jialin Zhang, Xiaoming Sun. Influence Maximization. 0.6. 0.5. 0.1. 0.4. 0.6. 0.1. 0.8. 0.1. A. B. C. D. E. F. Probability . of . influence. Marketer Alice. What you need to know about targeting, grooming and Child Sexual Exploitation. A guide for anyone working with young people . Before you go any further:. Fill in the survey. You need to know: . The grooming line. Machine Learning. April 13, 2010. Last Time. Review of Supervised Learning. Clustering. K-means. Soft K-means. Today. A brief look at Homework 2. Gaussian Mixture Models. Expectation Maximization. The Problem. Course info. Prereq. – CSE 2221 or CSE 222. Co-. req. – CSE . 2231. Website. http. ://www.cse.ohio-state.edu. /. ~shir/cse-. 2451. /. Brief history of C. 1970’s. Unix. C, from BCPL (Thompson and Ritchie. [X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= =xi]= E[X]= E[X]= Linearity of Expectation: E[X + Y] = E[X] + E[Y]Example: Birthday Paradoxm balls A All ZACA medicine cabinets are designed for recessed installation in a standard wall opening 14 B To replace an existing cabinet remove old Winter 2017. 1. Presentations on Monday. 2:30-4:20pm, Monday 3/13. No . more than 5 slides (including title slide. ). Time limit to be announced. Both partners should speak. Slides are due BY NOON (12pm) on Mon 3/13 to catalyst.