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Amazon Best Sellers Rank: #3 in Data Mining (Kindle Store) #4 in Big Data Businesses #5 in Information Management (Kindle Store) This book fills the need for a concise and conversational book on the growing field of Data Analytics and Big Data. Easy to read and informative, this lucid book covers everything important, with concrete examples, and invites the reader to join this field. The chapters in the book are organized for a typical one-semester course. The book contains case-lets from real-world stories at the beginning of every chapter. There is also a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Finally, it includes short tutorials for R & Weka platforms. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others attracted to the idea of discovering new insights and ideas from data can use this as a textbook. Professionals in various domains, including executives, managers, analysts, professors, doctors, accountants, and others can use this book to learn in a few hours how to make sense of and develop actionable insights from the enormous data coming their way. This is a flowing book that one can finish in one sitting, or one can return to it again and again for insights and techniques. Table of Contents Chapter 1: Wholeness of Data Analytics Chapter 2: Business Intelligence Concepts & Applications Chapter 3: Data Warehousing Chapter 4: Data Mining Chapter 5: Data Visualization Chapter 6: Decision Trees Chapter 7: Regression Models Chapter 8: Artificial Neural Networks Chapter 9: Cluster Analysis Chapter 10: Association Rule Mining Chapter 11: Text Mining Chapter 12: Web Mining Chapter 13: Big Data Chapter 14: Data Modeling Primer Appendix A: Data Mining Tutorial using Weka Appendix B: Data Mining Tuto

This book fills the need for a concise and conversational book on the growing field of Data Analytics and Big Data. Easy to read and informative, this lucid book covers everything important, with concrete examples, and invites the reader to join this field. The chapters in the book are organized for a typical one-semester course. The book contains case-lets from real-world stories at the beginning of every chapter. There is also a running case study across the chapters as exercises. This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Finally, it includes short tutorials for R & Weka platforms. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others attracted to the idea of discovering new insights and ideas from data can use this as a textbook. Professionals in various domains, including executives, managers, analysts, professors, doctors, accountants, and others can use this book to learn in a few hours how to make sense of and develop actionable insights from the enormous data coming their way. This is a flowing book that one can finish in one sitting, or one can return to it again and again for insights and techniques. Table of Contents Chapter 1: Wholeness of Data Analytics Chapter 2: Business Intelligence Concepts & Applications Chapter 3: Data Warehousing Chapter 4: Data Mining Chapter 5: Data Visualization Chapter 6: Decision Trees Chapter 7: Regression Models Chapter 8: Artificial Neural Networks Chapter 9: Cluster Analysis Chapter 10: Association Rule Mining Chapter 11: Text Mining Chapter 12: Web Mining Chapter 13: Big Data Chapter 14: Data Modeling Primer Appendix A: Data Mining Tutorial using Weka Appendix B: Data Mining Tutorial using R

Heard on the Street Reason for this report: Bits & Bytes on EVs – Part VII Please refer to important disclosures and disclaimers at the end of this report. Investors should consider this report as only a single factor in making their investment decision. Companies mentioned: IFX, STM, ON Semi, Rohm, Cree, Vanguard & Delta April 23, 2019 Research

SK Hynix: Revising estimates - Steeper decline but trough earnings in Q2'19 and more confidence of 2H improvement

In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have been found in many of the past forecasting models: (1) statistical assumptions about variables are required for time series models, such as the autoregressive moving average model (ARMA) and the autoregressive conditional heteroscedasticity (ARCH), to produce forecasting models of mathematical equations, and these are not easily understood by stock investors; and (2) the rules mined from some artificial intelligence (AI) algorithms, such as neural networks (NN), are not easily realized.

This study presents a hybrid AI (artificial intelligence) approach to the implementation of trading strategies in the S&P 500 stock index futures market. The hybrid AI approach integrates the rule-based systems technique and the neural networks technique to accurately predict the direction of daily price changes in S&P 500 stock index futures. By highlighting the advantages and overcoming the limitations of both the neural networks technique and rule-based systems technique, the hybrid approach can facilitate the development of more reliable intelligent systems to model expert thinking and to support the decision-making processes. Our methodology differs from other studies in two respects. First, the rule-based systems approach is applied to provide neural networks with training examples. Second, we employ Reasoning Neural Networks (RN) instead of Back Propagation Networks. Empirical results demonstrate that RN outperforms the other two ANN models (Back Propagation Networks and Perceptron). Based upon this hybrid AI approach, the integrated futures trading system (IFTS) is established and employed to trade the S&P 500 stock index futures contracts. Empirical results also confirm that IFTS outperformed the passive buy-and-hold investment strategy during the 6-year testing period from 1988 to 1993.

The next decade (present to ~ 2020–2025) could be characterized by large-scale labour disruption and further acceleration of income and wealth inequality due to the widespread introduction of general-purpose robotics, machine-learning software/artificial intelligence (AI) and their various interconnections within the emerging infrastructure of the ‘Internet of Things’ (IoT). In this paper I argue that such technological changes and their socioeconomic consequences signal the emergence of a global metasystem (i.e. control organization beyond markets and nation-states) and may require a qualitatively new level of political organization to guide a process of self-organization. Consequently, this paper proposes and attempts to develop a conceptual framework with the potential to aid an international political transition towards a ‘post-capitalist’ ‘post-nation state’ global world. This conceptual framework is grounded within sociotechnological theory of the ‘Global Brain’ (GB), which describes a potential future planetary organizational structure founded on distributed and open-ended intelligence; and the socioeconomic theory of the ‘Commons’, which is a paradigm describing distributed modes of organization founded upon principles of democratic management and open access. In the integration of GB theory and Commons theory this paper ultimately argues that an appropriate international response to the emerging technological revolution should include the creation of networks with both automated and collaborative components that function on ‘Global Commons’ (GC) logic (i.e. beyond both state and market logic).

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

There are instructions for performing each statistical test in SAS, as well. It's not as easy to use as the spreadsheets or web pages, but if you're going to be doing a lot of advanced statistics, you're going to have to learn SAS or a similar program sooner or later. I've got a page on the basics of SAS. Salvatore Mangiafico has written An R Companion to the Handbook of Biological Statistics, available as a free set of web pages and also as a free pdf. R is a free statistical programming language, useable on Windows, Mac, or Linux computers, that is becoming increasingly popular among serious users of statistics. If I were starting from scratch, I'd learn R instead of SAS and make my students learn it, too. Dr. Mangiafico's book provides example programs for nearly all of the statistical tests I describe in the Handbook, plus useful notes on getting started in R.

180417-2018WO0002-人工智慧法律暨智慧財產前瞻議題會議講義-v1F

Alpha（α）是第一個希臘字母，天文學中，Alpha是星座裡最亮的那顆星；動物學中，Alpha是領頭的那匹狼；現在，戰勝人類的AlphaGo，又帶來了顛覆一切、風頭浪尖的全新思考法。人工智慧全面來襲，顛覆職場、企業、產業、經濟與未來。AlphaGo贏的祕密是什麼？給了我們人類什麼啟示？

The AI Advantage How to Put the Artificial Intelligence Revolution to Work

15個成功上路的有感應用大蒐集，看AI如何改變你的生活 直擊AI學校！從高一生、榮總醫師到上市公司董事長都搶學的一堂課

The impact of new technology on public-sector service delivery and citizens’ attitudes about government has long been debated by political observers. This article assesses the consequences of egovernment for service delivery, democratic responsiveness, and public attitudes over the last three years. Research examines the content of e-government to investigate whether it is taking advantage of the interactive features of the World Wide Web to improve service delivery, democratic responsiveness, and public outreach. In addition, a national public opinion survey examines the ability of e-government to influence citizens’ views about government and their confidence in the effectiveness of service delivery. Using both Web site content as well as public assessments, I argue that, in some respects, the e-government revolution has fallen short of its potential to transform service delivery and public trust in government. It does, however, have the possibility of enhancing democratic responsiveness and boosting beliefs that government is effective.

 MaaS (Mobility as a Service) (Mobility as a Service) (Mobility as a Service) (Mobility as a Service) (Mobility as a Service)(Mobility as a Service) (Mobility as a Service) (Mobility as a Service)(Mobility as a Service)(Mobility as a Service)  以運具使用權替代所有的輸服務概念  提供 O2O O2O O2O 及D2D 多元運具整合接駁服務  應用情境  依用路人之需求，透過手機 APP APP結合交通、天氣生活服務等 提供甲地到乙旅程規劃及多元運具組合方案  可利用手機線上訂購 所需組合方案，並完成支付  可透過 手機掌握所有旅程

•淺談共享經濟 •物流業已存在共享經濟? •物流聯盟的商業模式 •物流共享經濟2.0及成功案例

“AI+醫療”本來是輕資產，做著做著大家一窩蜂做肺結節、乳腺癌、眼底等等，最後大家 的檢測率可能都差不多了，AI就變成了軟件，AI公司也變成了軟件公司。軟件讓醫院採購，誰 的價格低就給誰，你還想按人頭收費？醫院要么直接買你的軟件，要么跟硬件結合，最後又成 了傳統的公司，撐不住AI公司那麼大的估值。最近看到某醫療投資大咖的以上觀點，很是讚 同！據相關人士透露，國家藥品監督管理局已經理清了AI審批全流程的思路，審批通道已於 2018年12月中旬開放，然而，高標準下，還無一企業進行AI三類器械產品申報。2018年已經過 完，不知道去年對投資人說今年拿證的企業會怎樣解釋。AI也從2016年的“超越人類”，到2018 年的“輔助人類”回歸理性。聯想到互聯網醫療當初要革醫院的命，結果很悲慘。 隨著近幾年AI投資熱潮，資本大量引入，中國在AI領域快速訓練了很多人工智能工程師，據 說，目前世界上43%的人工智能的論文作者中都有華人身影。醫療AI創業團隊背景相對較好， 高學歷、BAT背景，起初，投資人看第一個團隊的時候感覺背景很好，後來看看其他團隊也感 覺很好，一個一個項目也相繼成功融資，這時候投資人就感覺不對了，原來以為高技術門檻的 AI變成資本驅動了，原來AI應用層的壁壘沒這麼高！

区块链+共享经济创新发展研究报告-人民创投-2019.4-34页

2019中国物联网产业全景图谱报告（完整版）-2019.4-274页

6 Tips For Speaking Natural English_Speak English With Vanessa