Foundations of Finance: The Logic and Practice of Financial Management (10版)

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書名：Foundations of Finance: The Logic and Practice of Financial Management 10/E 作者：Keown/Martin/Petty 出版社：PEARSON 出版日期：2019/10/00 ISBN：9781292318738 內容簡介 目錄 PART I: THE SCOPE AND ENVIRONMENT OF FINANCIAL MANAGEMENT 1. An Introduction to the Foundations of Financial Management 2. The Financial Markets and Interest Rates 3. Understanding Financial Statements and Cash Flows 4. Evaluating a Firm’s Financial Performance PART II: THE VALUATION OF FINANCIAL ASSETS 5. The Time Value of Money 6. The Meaning and Measurement of Risk and Return 7. The Valuation and Characteristics of Bonds 8. The Valuation and Characteristics of Stock 9. The Cost of Capital PART III: INVESTMENT IN LONG-TERM ASSETS 10. Capital-Budgeting Techniques and Practice 11. Cash Flows and Other Topics in Capital Budgeting PART IV: CAPITAL STRUCTURE AND DIVIDEND POLICY 12. Determining the Financing Mix 13. Dividend Policy and Internal Financing PART V: WORKING-CAPITAL MANAGEMENT AND INTERNATIONAL BUSINESS FINANCE 14. Short-Term Financial Planning 15. Working-Capital Management 16. International Business Finance 17. Cash, Receivables, and Inventory Management

Artificial Intelligence: Foundations of Computational Agents (3版)

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【簡介】 Introduces concepts at the right level of detail, using straightforward language and minimum mathematics Examines the social and ethical impacts of AI, connecting techniques to real-world benefits and harms Explains state-of-the-art algorithms and examples underlying the theory, demonstrating how concepts are applied Provides two complementary software systems – AIPython and AILog - for experimentation and extension Pedagogical features include examples, end-of-chapter reviews, further reading lists, and exercises 【目錄】 Part I - Agents in the World Chapter 1 - Artificial Intelligence and Agents Chapter 2 - Agent Architectures and Hierarchical Control Part II - Reasoning and Planning with Certainty Chapter 3 - Searching for Solutions Chapter 4 - Reasoning with Constraints Chapter 5 - Propositions and Inference Chapter 6 - Deterministic Planning Part III - Learning and Reasoning with Uncertainty Chapter 7 - Supervised Machine Learning Chapter 8 - Neural Networks and Deep Learning Chapter 9 - Reasoning with Uncertainty Chapter 10 - Learning with Uncertainty Chapter 11 - Causality Part IV - Planning and Acting with Uncertainty Chapter 12 - Planning with Uncertainty Chapter 13 - Reinforcement Learning Chapter 14 - Multiagent Systems Part V - Representing Individuals and Relations Chapter 15 - Individuals and Relations Chapter 16 - Knowledge Graphs and Ontologies Chapter 17 - Relational Learning and Probabilistic Reasoning Part VI - The Big Picture Chapter 18 - The Social Impact of Artificial Intelligence Chapter 19 - Retrospect and Prospect Appendix A - Mathematical Preliminaries and Notation Appendix B - Mapping to Open-Source Packages