EM623-Week4a Carlo Lipizzi clipizzi@stevens.edu SSE 2016 Machine Learning and Data Mining Data mining specific tools: introduction to R with Rattle GUI • 6th survey since 2007 • 68 questions • 10,000+ invitations emailed, plus promoted by newsgroups, vendors, and bloggers • Respondents: 1,259 data miners from 75 countries • Data collected in first half of […]
IT enabled Business Intelligence, CRM, Database Applications Sep-18 Information Theory & Preparing Data Prof. Vibs Abhishek The Paul Merage School of Business University of California, Irvine BANA 273 Session 3 1 © Prof. V Choudhary, September 18 Agenda Information Theory Reminders Assignment 1 posted on Canvas Form groups for project Install Weka Working with Data
Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright c© 2016. All rights reserved. Draft of August 7, 2017. CHAPTER 7 Logistic Regression Numquam ponenda est pluralitas sine necessitate ‘Plurality should never be proposed unless needed’ William of Occam We turn now to a second algorithm for classification called multinomial lo- gistic regression,
1) A Data Mining project report should be closer to a scientific or engineer document than a business document. It should be objective and supported by quantitative results. In many cases a DM project report will be a portion of or an appendix to a broader business report (e.g. Marketing Plan, Program Roll-out Plan, Fraud
CSI 4506: Introduction à l’Intelligence Artificielle * CSC 589: ROC Analysis (Based on ROC Graphs: Notes and Practical Considerations for Data Mining Researchers by Tom Fawcett, January 2003. * Common Evaluation Measures 1. Confusion Matrix True Class Hypothe- Sized Class Positive Negative Yes True Positives (TP) False Positives (FP) No False Negatives (FN) True Negatives
EM623-Week3 Carlo Lipizzi clipizzi@stevens.edu SSE Machine Learning and Data Mining Data management: generalized tools and techniques Knowledge Discovery Process, in practice Data Preparation estimated to take 70- 80% of the time and effort Data Preparation 2 Data Processing Flow Data Analysis Decisions Quality of Data Quality of Analysis Quality of Decisions • Types of Data
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Chapter 1: Introduction COMP9313: Big Data Management Lecturer: Xin Cao Course web site: http://www.cse.unsw.edu.au/~cs9313/ 6.‹#› 1 Chapter 6: Spark 6.‹#› Part 1: Spark Introduction 6.‹#› Motivation of Spark MapReduce greatly simplified big data analysis on large, unreliable clusters. It is great at one-pass computation. But as soon as it got popular, users wanted more: More
2dw 1 COMP9318: Data Warehousing and Data Mining — L2: Data Warehousing and OLAP — 2 n Why and What are Data Warehouses? Data Analysis Problems n The same data found in many different systems n Example: customer data across different departments n The same concept is defined differently n Heterogeneous sources n Relational DBMS,
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Presentation_final Combinational Collaborative Filtering: An Approach For Personalised, Contextually Relevant Product Recommendation Baskets Research Project – Jai Chopra (338852) Dr Wei Wang (Supervisor) Dr Yifang Sun (Assessor) • Recommendation engines are now heavily used online • 35% of Amazon purchases are from algorithms • We would like to extend on current implementations and provide some
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Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright c© 2016. All rights reserved. Draft of August 7, 2017. CHAPTER 6 Naive Bayes and SentimentClassification Classification lies at the heart of both human and machine intelligence. Deciding what letter, word, or image has been presented to our senses, recognizing faces or voices, sorting