Introduction to Machine Learning and Data Mining Introduction to Machine Learning and Data Mining COMP9417 Machine Learning and Data Mining Last revision: 28 Feb 2018 COMP9417 ML & DM Intro to ML & DM Semester 1, 2018 1 / 94 Acknowledgements Material derived from slides for the book “Elements of Statistical Learning (2nd Ed.)” by […]
CS373 Data Mining and� Machine Learning� Lecture 3 Jean Honorio Purdue University (originally prepared by Tommi Jaakkola, MIT CSAIL) Today’s topics • Quick review of support vector machines… • Need for more powerful classifiers • Feature mappings, non-linear classifiers, kernels - non-linear feature mappings - kernels, kernel perceptron Linear classifiers (with offset) • A linear classifier with parameters decision boundary +
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Introduction to Information Retrieval Online edition (c) 2009 Cambridge UP An Introduction to Information Retrieval Draft of April 1, 2009 Online edition (c) 2009 Cambridge UP Online edition (c) 2009 Cambridge UP An Introduction to Information Retrieval Christopher D. Manning Prabhakar Raghavan Hinrich Schütze Cambridge University Press Cambridge, England Online edition (c) 2009 Cambridge UP
Data Mining and Machine Learning Fall 2018, Homework 2 (due on Sep 11, 11.59pm EST) Jean Honorio jhonorio@purdue.edu The homework is based on a total of 10 points. Your code should be in Python 2.7. For clarity, the algorithms presented here will assume zero-based indices for arrays, vectors, matrices, etc. Please read the submission instructions
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About COMP9318 (2018 s1) Wei Wang @ CSE, UNSW February 24, 2018 Wei Wang @ CSE, UNSW About COMP9318 (2018 s1) Introduction Lecturer-in-charge: Prof. Wei Wang School of Computer Science and Engineering Office: K17 507 E-mail: weiw@cse Ext: 9385 7162 http: // www. cse. unsw. edu. au/ ~ weiw Research Interests: Knowledge graph / natural
EM623-Week12 Text Mining Carlo Lipizzi clipizzi@stevens.edu SSE 2016 Reasons for Text Mining • 85-90 percent of all corporate data is in some kind of unstructured form (e.g., text) • Unstructured corporate data is doubling in size every 18 months • Tapping into these information sources is not an option, but a need to stay competitive
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CS373 Data Mining and� Machine Learning� Lecture 2 Jean Honorio Purdue University (originally prepared by Tommi Jaakkola, MIT CSAIL) Today’s topics • Perceptron, convergence - the prediction game - mistakes, margin, and generalization • Maximum margin classifier — support vector machine - estimation, properties - allowing misclassified points Recall: linear classifiers • A linear classifier (through origin) with parameters divides the
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untitled INV ITED P A P E R Real-Time Wireless Sensor-Actuator Networks for Industrial Cyber-Physical Systems Despite their success in industrial monitoring applications, wireless sensor actuator network (WSAN) technologies face significant challenges in supporting control systems. This paper surveys recent advances in real-time WSANs for industrial control systems. By Chenyang Lu, Senior Member IEEE, Abusayeed
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About COMP9318 (2018 s1) Wei Wang @ CSE, UNSW February 24, 2018 Wei Wang @ CSE, UNSW About COMP9318 (2018 s1) Introduction Lecturer-in-charge: Prof. Wei Wang School of Computer Science and Engineering Office: K17 507 E-mail: weiw@cse Ext: 9385 7162 http: // www. cse. unsw. edu. au/ ~ weiw Research Interests: Knowledge graph / natural
Data Mining and Machine Learning Fall 2018, Homework 3 (due on Sep 23, 11.59pm EST) Jean Honorio jhonorio@purdue.edu The homework is based on a total of 10 points. Your code should be in Python 2.7. For clarity, the algorithms presented here will assume zero-based indices for arrays, vectors, matrices, etc. Please read the submission instructions
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