PPT-Big Data, Bigger Audience: A Meta-algorithm for Making Machine Learning Actionable for

Author : giovanna-bartolotta | Published Date : 2018-03-17

Dylan Cashman Remco Chang Visual Analytics Lab at Tufts VALT Tufts University Medford MA Stephen Kelley Diane Staheli Cody Fulcher Marianne Procopio MIT Lincoln

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Big Data, Bigger Audience: A Meta-algorithm for Making Machine Learning Actionable for: Transcript

Dylan Cashman Remco Chang Visual Analytics Lab at Tufts VALT Tufts University Medford MA Stephen Kelley Diane Staheli Cody Fulcher Marianne Procopio MIT Lincoln Laboratory Lexington MA. for Scaling Sparse Optimization. Tyler B. Johnson and Carlos . Guestrin. University of Washington. Very important to machine learning. Our focus is constrained convex optimization. Number of constraints can be very large!. William Cohen. Outline. Intro. Who, Where, When - . administrivia. What/How. Course outline & load. Resources – languages and machines. Java (for . Hadoop. ). Small machines – understand essence of scaling. Clustering and pattern recognition. W. ikipedia entry on machine learning. 7.1 Decision tree learning. 7.2 Association rule learning. 7.3 Artificial neural networks. 7.4 Genetic programming. 7.5 Inductive logic programming. Completely Different. (again). Software Defined Intelligence. A New Interdisciplinary Approach to Intelligent Infrastructure. David Meyer. Networking Field Day 8. http://techfieldday.com/event/nfd8/. g and . aCGH. denoising. . Charalampos (Babis) E. Tsourakakis. ctsourak@math.cmu.edu. . Machine Learning Seminar. . . 10. th. January ‘11. Machine Learning Lunch Seminar. Huang, Ph.D., Professor. Email. ：. yhuang@nju.edu.cn. NJU-PASA Lab for Big Data Processing. Department of Computer Science and Technology. Nanjing University. May 29, 2015, India. A Unified Programming Model . William Cohen. Outline. Intro. Who, Where, When - . administrivia. What/How. Proposed outline & load. Resources – languages and machines. Java (for . Hadoop. ). Small machines – understand essence of scaling. R/Finance. 20 May 2016. Rishi K Narang, Founding Principal, T2AM. What the hell are we talking about?. What the hell is machine learning?. How the hell does it relate to investing?. Why the hell am I mad at it?. David Kauchak. CS 451 – Fall 2013. Why are you here?. What is Machine Learning?. Why are you taking this course?. What topics would you like to see covered?. Machine Learning is…. Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.. CS539. Prof. Carolina Ruiz. Department of Computer Science . (CS). & Bioinformatics and Computational Biology (BCB) Program. & Data Science (DS) Program. WPI. Most figures and images in this presentation were obtained from Google Images. William Cohen. Outline. Intro. Who, Where, When - . administrivia. What/How. Course outline & load. Resources – languages and machines. Java (for . Hadoop. ). Small machines – understand essence of scaling. Lecture 02 . – . PAC Learning and tail bounds intro. CS 790-134 Spring 2015. Alex Berg. Today’s lecture. PAC Learning. Tail bounds…. Rectangle learning. +. -. -. -. -. -. -. +. +. +. Hypothesis . John Windle MD. Professor of Cardiovascular Medicine. Richard and Mary Holland Distinguished Chair of Cardiovascular Science. Disclosures:. This work is supported, in part, from AHRQ R-01 grant HS22110-01A1. Sylvia Unwin. Faculty, Program Chair. Assistant Dean, iBIT. Machine Learning. Attended TDWI in Oct 2017. Focus on Machine Learning, Data Science, Python, AI. Started with a catchy opening speech – “BS-Free AI For Business”.

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