机器学习英文版

Table of Contents I: INTRODUCTION AND OVERVIEW .......................................6 Summary......................................................................................................................7 Introduction to Big Data and Machine Learning .........................................................9 Classification of Alternative Data Sets......................................................................12 Classification of Machine Learning Techniques........................................................16 Positioning within the Big Data Landscape ...............................................................21 II: BIG AND ALTERNATIVE DATA ........................................25 Overview of Alternative Data....................................................................................26 Data from Individual Activity....................................................................................30 Data from Business Processes....................................................................................38 Data from Sensors......................................................................................................42 III: MACHINE LEARNING METHODS....................................51 Overview of Machine Learning Methods..................................................................52 Supervised Learning: Regressions.............................................................................57 Supervised Learning: Classifications.........................................................................77 Unsupervised Learning: Clustering and Factor Analyses..........................................93 Deep and Reinforcement Learning ..........................................................................102 Comparison of Machine Learning Algorithms ........................................................117 IV: HANDBOOK OF ALTERNATIVE DATA .........................135 Table of contents of data providers..........................................................................136 A. Data from Individual Activity.............................................................................137 B. Data from Business Processes.............................................................................147 C. Data from Sensors...............................................................................................176 D. Data Aggregators................................................................................................189 E. Technology Solutions..........................................................................................191 APPENDIX.............................................................................214 Techniques for Data Collection from Websites.......................................................215 Packages and Codes for Machine Learning .............................................................226 Mathematical Appendices........................................................................................231 References................................................................................................................254 Glossary ...................................................................................................................270 of Alternative Data Sets......................................................................12 Classification of Machine Learning Techniques........................................................16 Positioning within the Big Data Landscape ...............................................................21 II: BIG AND ALTERNATIVE DATA ........................................25 Overview of Alternative Data....................................................................................26 Data from Individual Activity....................................................................................30 Data from Business Processes....................................................................................38 Data from Sensors......................................................................................................42 III: MACHINE LEARNING METHODS....................................51 Overview of Machine Learning Methods..................................................................52 Supervised Learning: Regressions.............................................................................57 Supervised Learning: Classifications.........................................................................77 Unsupervised Learning: Clustering and Factor Analyses..........................................93 Deep and Reinforcement Learning ..........................................................................102 Comparison of Machine Learning Algorithms ........................................................117 IV: HANDBOOK OF ALTERNATIVE DATA .........................135 Table of contents of data providers..........................................................................136 A. Data from Individual Activity.............................................................................137 B. Data from Business Processes.............................................................................147 C. Data from Sensors...............................................................................................176 D. Data Aggregators................................................................................................189 E. Technology Solutions..........................................................................................191 APPENDIX.............................................................................214 Techniques for Data Collection from Websites.......................................................215 Packages and Codes for Machine Learning .............................................................226 Mathematical Appendices........................................................................................231 References................................................................................................................254 Glossary ...................................................................................................................270