Latent Structure and Causality: Inference from Data (1版)

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【簡介】 Inferring latent structure and causality is crucial for understanding underlying patterns and relationships hidden in the data. This book covers selected models for latent structures and causal networks and inference methods for these models.After an introduction to the EM algorithm on incomplete data, the book provides a detailed coverage of a few widely used latent structure models, including mixture models, hidden Markov models, and stochastic block models. EM and variation EM algorithms are developed for parameter estimation under these models, with comparison to their Bayesian inference counterparts. We make further extensions of these models to related problems, such as clustering, motif discovery, Kalman filtering, and exchangeable random graphs. Conditional independence structures are utilized to infer the latent structures in the above models, which can be represented graphically. This notion generalizes naturally to the second part on graphical models that use graph separation to encode conditional independence. We cover a variety of graphical models, including undirected graphs, directed acyclic graphs (DAGs), chain graphs, and acyclic directed mixed graphs (ADMGs), and various Markov properties for these models. Recent methods that learn the structure of a graphical model from data are reviewed and discussed. In particular, DAGs and Bayesian networks are an important class of mathematical models for causality. After an introduction to causal inference with DAGs and structural equation models, we provide a detailed review of recent research on causal discovery via structure learning of graphs. Finally, we briefly introduce the causal bandit problem with sequential intervention.

Personal Data (Privacy) Law in Hong Kong: A Practical Guide on Compliance (Third Edition) (1版)

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【簡介】 　　With an increasing concern of personal data privacy in Hong Kong and around the world，the Office of the Privacy Commissioner for Personal Data (PCPD)，Hong Kong publishes this third edition of Personal Data (Privacy) Law in Hong Kong: A Practical Guide on Compliance to provide practical guidance on the law of privacy，and the ways to protect personal data in everyday life。 　　This third edition will guide readers through the recent developments of personal data privacy in the local，regional and global contexts with updates on recent Administrative Appeals Board and Court decisions，and investigation reports and materials from the PCPD。 In addition，some most-concerned issues are discussed，such as ethical development and use of AI，the collection and use of personal data by employers during COVID-19，data protection under work-from-home arrangements，data security measures for ICT systems，guidelines on doxxing offences and data breach handling。 This book will certainly provide readers a comprehensive overview of current situation of personal data privacy issue。   【目錄】 Chapter 1 Introduction Chapter 2 The Meaning of “Personal Data” Chapter 3 The Meaning of “Collect” Chapter 4 The Meaning of “Data User” Chapter 5 Data Protection Principle 1 Chapter 6 Data Protection Principle 2 Chapter 7 Data Protection Principle 3 Chapter 8 Data Protection Principle 4 Chapter 9 Data Protection Principle 5 Chapter 10 Data Protection Principle 6(a) to (d) and the Data Access Provisions in Part 5 Chapter 11 Data Protection Principle 6(e) to (g) and the Data Correction Provisions in Part 5 Chapter 12 Exemption Provisions in Part 8 Chapter 13 The Commissioner’s Statutory Duties in Investigations Chapter 14 Data Breach Handling and Notifications Chapter 15 Criminal Offences Chapter 16 Doxxing Chapter 17 Cross-border Transfers of Personal Data from Hong Kong Chapter 18 An Overview of the Mainland’s Personal Information Protection Regime Appendix I Selected Case Notes on Court Judgments Appendix II Major Differences between the PIPL, the GDPR and the PDPO Appendix III Checklist for Data Users in Ensuring Compliance with the Ordinance Appendix IV Data Subject’s Rights when his Personal Data Privacy is Infringed   看更多

Certiport ITS Data Analytics資料分析核心能力國際認證應考攻略 (1版)

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【簡介】 Information Technology Specialist（ITS）是由Pearson VUE／Certiport推出符合產業趨勢的資訊科技認證，涵蓋IT資訊技術、資料庫、軟體研發、新興科技四大領域，透過 ITS 各項認證指標訓練，可驗證考生是否確實掌握業界所需與具備雇主所需的 IT 技能，幫助考生為未來職涯做好準備。 本書整理了「ITS Data Analytics 資料分析」認證考科綱要所涵蓋的資訊技術與電腦技能，考生可透過精進學習本書各章節重點內容，迅速掌握應考方向。 【目錄】 CH01 資料基礎 Data Basics CH02 資料操作 Data Manipulat CH03 資料分析 Data Analysis CH04 資料視覺化與溝通 Data Visualization and Communication CH05 負責任的分析實踐 Responsible Analytics Practices CH06 模擬試題 CH07 ITS 資訊科技專家國際認證原廠認證應考資訊

Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (1版)

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【簡介】 Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science.Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven resultsTeaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist’s decision-making processCultivates critical thinking throughout the entire data science life cycleProvides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutionsSuitable for advanced undergraduate and graduate students, domain scientists, and practitioners