PPT-Distributed Machine Learning

COS 518 Advanced Computer Systems Lecture 13 Daniel Suo Outline 2 What is machine learning Why is machine learning hard in parallel distributed systems A brief history of what people have done. Spring . 2013. Rong. Jin. 2. CSE847 Machine Learning. Instructor: . Rong. Jin. Office Hour: . Tuesday 4:00pm-5:00pm. TA, . Qiaozi. . Gao. , . Thursday 4:00pm-5:00pm. Textbook. Machine Learning. The Elements of Statistical Learning. Lecture 6. K-Nearest Neighbor Classifier. G53MLE . Machine Learning. Dr . Guoping. Qiu. 1. Objects, Feature Vectors, Points. 2. Elliptical blobs (objects). 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 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. Lecture . 4. Multilayer . Perceptrons. G53MLE | Machine Learning | Dr Guoping Qiu. 1. Limitations of Single Layer Perceptron. Only express linear decision surfaces. G53MLE | Machine Learning | Dr Guoping Qiu. Jimmy Lin and Alek . Kolcz. Twitter, Inc.. Presented by: Yishuang Geng and Kexin Liu. 2. Outline. •Is twitter big data? . •How . can machine learning help twitter?. •Existing challenges?. •Existing literature of large-scale learning. 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. Corey . Pentasuglia. Masters Project. 5/11/2016. Examiners. Dr. Scott . Spetka. Dr. . Bruno . Andriamanalimanana. Dr. Roger . Cavallo. Masters Project Objectives. Research DML (Distributed Machine Learning). Bahrudin Hrnjica, MVP. Agenda. Intro to ML. Types of ML. dotNET and ML-tools and libraries. Demo01: ANN with C#. Demo02: GP with C#. .NET Tools – Acord.NET, GPdotNET. Summary. Machine Learning?. method of teaching computers to make predictions based on data.. Page 46 L istening to the voice of customers plays a prominent role in a customer-centric business strategy. But with the business environment’s increased complexity and dynamism for a customer- Berrin Yanikoglu. Slides are expanded from the . Machine Learning-Mitchell book slides. Some of the extra slides thanks to T. Jaakkola, MIT and others. 2. CS512-Machine Learning. Please refer to . http. Gihyuk Ko. PhD Student, Department of Electrical and Computer Engineering. Carnegie Mellon University. November. 14, 2016. *some slides were borrowed from . Anupam. . Datta’s. MIT Big . Data@CSAIL. Ryan Ma . Background and Purpose of the Project. Aerodynamic analysis is one of the most crucial traits of a vehicle. It affects the fuel consumption of a car. . The shape of the car significantly affects the aerodynamic performances, which includes the lift and the drag. .