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In this volume, Kieran McGroarty provides a philosophical commentary on a section of the Enneads written by the last great Neoplatonist thinker, Plotinus. The treatise is entitled “Concerning Well-Being” and was written at a late stage in Plotinus’ life when he was suffering from an illness that was shortly to kill him. Its main concern is with the good man and how he should pursue the good life. The treatise is therefore central to our understanding of Plotinus’ ethical theory, and the commentary seeks to explicate and elucidate that theory. Plotinus’ views on how one should live in order to fulfill oneself as a human being are as relevant now as they were in the third century AD. All Greek and Latin is translated, while short summaries introducing the content of each chapter help to make Plotinus’ argument clear even to the non-specialist.
This comprehensive introductory text with readings offers a historical overview of all major subdivisions of Western Philosophy perspectives–including both the analytic and Continental traditions–as well as Eastern philosophy, postcolonial philosophy, and feminist philosophy. Written in an engaging and captivating style, it makes philosophy accessible without oversimplifying the material, and shows that philosophy’s powerful ideas affect the lives of real people.
Computer science and artificial intelligence in particular have no curriculum in research methods, as other sciences do. This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in data, experiment designs and hypothesis-testing tools to help data speak convincingly, and modeling tools to help explain data. Although many of these techniques are statistical, the book discusses statistics in the context of the broader empirical enterprise. The first three chapters introduce empirical questions, exploratory data analysis, and experiment design. The blunt interrogation of statistical hypothesis testing is postponed until chapters 4 and 5, which present classical parametric methods and computer-intensive (Monte Carlo) resampling methods, respectively. This is one of few books to present these new, flexible resampling techniques in an accurate, accessible manner.Much of the book is devoted to research strategies and tactics, introducing new methods in the context of case studies. Chapter 6 covers performance assessment, chapter 7 shows how to identify interactions and dependencies among several factors that explain performance, and chapter 8 discusses predictive models of programs, including causal models. The final chapter asks what counts as a theory in AI, and how empirical methods—which deal with specific systems—can foster general theories.Mathematical details are confined to appendixes and no prior knowledge of statistics or probability theory is assumed. All of the examples can be analyzed by hand or with commercially available statistics packages.The Common Lisp Analytical Statistics Package (CLASP), developed in the author’s laboratory for Unix and Macintosh computers, is available from The MIT Press.A Bradford Book
ما از اخلاق چه انتظاری داریم؟ این سوال شاید تاحدودی برای عموم مردم و یا متخصصان این رشته قابل پاسخ باشد؛ اینکه خوب بودن را به ما بیاموزد و در تنظیم روابط فردی م...
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