1intro 1 COMP9318: Data Warehousing and Data Mining — L1: Introduction — 2 Chapter 1. Introduction n Motivation: Why data mining? n What is data mining? n Data Mining: On what kind of data? n Data mining functionality n Are all the patterns interesting? n Classification of data mining systems n Major issues in data […]
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Introduction to Data Wrangling Introduction to Data Wrangling Faculty of Information Technology Monash University FIT5196 week 1 (Monash) FIT5196 1 / 38 Outline 1 Motivations 2 Introduction to FIT5196 Data Wrangling Unit structure Assessments Unit management 3 Introduction to Data Wrangling Data Quality Problems Characteristics of Tidy Data Major Tasks in Data Wrangling Programming Environment
Ensemble Lecture 11: Online Learning Prof. Michael R. Lyu Computer Science & Engineering Dept. The Chinese University of Hong Kong 1 CMSC5741 Big Data Tech. & Apps. 1 A Motivating Example– Spam Filtering 2 Incoming Emails Spams Not Spams Spam Filter Incoming Emails Spams Not Spams Spam Filter Traditional Method: Training 3 Feature extraction: X
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Springer Texts in Statistics An Introduction to Statistical Learning Gareth James Daniela Witten Trevor Hastie Robert Tibshirani with Applications in R Springer Texts in Statistics Series Editors: G. Casella S. Fienberg I. Olkin For further volumes: http://www.springer.com/series/417 http://www.springer.com/series/417 Gareth James • Daniela Witten • Trevor Hastie Robert Tibshirani An Introduction to Statistical Learning with Applications
G52SWM Lecture 03 OO Analysis/Design with UML Peer-Olaf Siebers Please note that the slides published AFTER the lectures and workshops are the official slides and are the ones that should be used for revision. Topics • Lecture – OO Analysis and Design (OOA/D) with UML – Lab reflections • Lab: – Group activity: Designing a
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MPT_CAPM_Lecture Economics 403A Review of MPT and CAPM Part I Portfolio Returns with Two Risky Securities Dr. Randall R. Rojas 1 Today’s Class (Part I) • Modern Portfolio Concepts • Portfolio Return and Expected Return • Covariance and Correlation • Portfolio Variance and Standard Deviation • Diversification with Two Assets – Fixed Weights – Varying
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1.dvi D RA FT Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition. Daniel Jurafsky & James H. Martin. Copyright c© 2006, All rights reserved. Draft of June 25, 2007. Do not cite without permission. 1 INTRODUCTION Dave Bowman: Open the pod bay doors, HAL. HAL: I’m sorry Dave,
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites
UCLA Professor Lee Ohanian Notes on Taxes and Government Spending, and the Impact on Economic Activity We will analyze two types of government spending: purchases of goods and services, which range from national defense expenditures to parks, to roads and bridges, and transfer payments, which is the act of the government transfering income from one
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Lee Ohanian Economics 402 UCLA 1 Notes on Business Cycles Business cycles are persistent movements in macroeconomic variables above or below their trend values. The trend is usually defined as either a log-linear trend, in which the cyclical component is the residual of a regression of the log of variable Y on time and a
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