Written to aid electrical engineers, computer engineers, and computer scientists, the volume includes: KEY FEATURES: the computer architecture, organization, and design associated with computer hardware - the various digital components used in the organization and design of digital computers - detailed steps that a designer must go through in order to design an elementary basic computer - the organization and architecture of the central processing unit - the organization and architecture of input-output and memory - the concept of multiprocessing - two new chapters on pipeline and vector processing - two sections devoted completely to the reduced instruction set computer (RISC) - and sample worked-out problems to clarify topics.

    Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself.

    Das' Sixth Edition of PRINCIPLES OF FOUNDATION ENGINEERING maintains the careful balance of current research and practical field applications that has made it the leading text in foundation engineering courses. Featuring a wealth of worked-out examples and figures that help students with theory and problem-solving skills, the book introduces civil engineering students to the fundamental concepts and application of foundation analysis design. Throughout, Das emphasizes the judgment needed to properly apply the theories and analysis to the evaluation of soils and foundation design as well as the need for field experience. The sixth edition contains many new homework and worked-out problems.

    Best-selling author D.S. Malik employs a student-focused approach, using complete programming examples to teach introductory programming concepts. This third edition has been enhanced to further demonstrate the use of OOD methodology, to introduce sorting algorithms (bubble sort and insertion sort), and to present additional material on abstract classes. In addition, the exercise sets at the end of each chapter have been expanded, and now contain several calculus and engineering-related exercises. Finally, all programs have been written, compiled, and quality-assurance tested with Microsoft Visual C++ .NET, available as an optional compiler with this text.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    It discusses neuroanatomy, cell and molecular mechanisms and signaling through a cognitive approach to behaviour. It features an expanded treatment of the nervous system, neurological and psychiatric diseases and perception.

    The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Written for the engineer as well as the race car enthusiast, the authors, who developed many of the original vehicle dynamics theories and principles covered in this book, including the Moment Method, pair analysis and lap time simulation, include much information that is not available in any other vehicle dynamics text.

    Now in it's tenth edition, this text makes chemistry exciting to students by showing them why important concepts are relevant to their lives and future careers.

    Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    The layout of the book covers the following: Laplace transforms, mathematical model

    Bryan Derrickson of Valencia Community College in Orlando, Florida joins Jerry Tortora as a co-author, bringing his background and expertise in physiology in balance with Jerry's focus on anatomy. The authors have maintained in the text the superb balance between structure and function and continue to emphasize the correlations between normal physiology and pathophysiology, normal anatomy and pathology, and homeostasis and homeostatic imbalances. The acclaimed illustration program is now even better thanks to the input of hundreds of professors and students and the re-development of many of the figures depicting the toughest topics for students to grasp. The eleventh edition now fully integrates this exceptional text with a host of innovative electronic media, setting the standard once again for a rewarding and successful classroom experience for both students and instructors.

    Do you want your students to just take psychology or to experience psychology? Laura King's approach to introductory psychology embodies a balanced consideration of functioning behavior as well as dysfunction and a view of psychology as an integrated whole.