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| computer science ucla requirements: Stuck in the Shallow End, updated edition Jane Margolis, 2017-03-03 Why so few African American and Latino/a students study computer science: updated edition of a book that reveals the dynamics of inequality in American schools. The number of African Americans and Latino/as receiving undergraduate and advanced degrees in computer science is disproportionately low. And relatively few African American and Latino/a high school students receive the kind of institutional encouragement, educational opportunities, and preparation needed for them to choose computer science as a field of study and profession. In Stuck in the Shallow End, Jane Margolis and coauthors look at the daily experiences of students and teachers in three Los Angeles public high schools: an overcrowded urban high school, a math and science magnet school, and a well-funded school in an affluent neighborhood. They find an insidious “virtual segregation” that maintains inequality. The race gap in computer science, Margolis discovers, is one example of the way students of color are denied a wide range of occupational and educational futures. Stuck in the Shallow End is a story of how inequality is reproduced in America—and how students and teachers, given the necessary tools, can change the system. Since the 2008 publication of Stuck in the Shallow End, the book has found an eager audience among teachers, school administrators, and academics. This updated edition offers a new preface detailing the progress in making computer science accessible to all, a new postscript, and discussion questions (coauthored by Jane Margolis and Joanna Goode). |
| computer science ucla requirements: Operating Systems Remzi H. Arpaci-Dusseau, Andrea C. Arpaci-Dusseau, 2018-09 This book is organized around three concepts fundamental to OS construction: virtualization (of CPU and memory), concurrency (locks and condition variables), and persistence (disks, RAIDS, and file systems--Back cover. |
| computer science ucla requirements: Modeling Life Alan Garfinkel, Jane Shevtsov, Yina Guo, 2017-09-06 This book develops the mathematical tools essential for students in the life sciences to describe interacting systems and predict their behavior. From predator-prey populations in an ecosystem, to hormone regulation within the body, the natural world abounds in dynamical systems that affect us profoundly. Complex feedback relations and counter-intuitive responses are common in nature; this book develops the quantitative skills needed to explore these interactions. Differential equations are the natural mathematical tool for quantifying change, and are the driving force throughout this book. The use of Euler’s method makes nonlinear examples tractable and accessible to a broad spectrum of early-stage undergraduates, thus providing a practical alternative to the procedural approach of a traditional Calculus curriculum. Tools are developed within numerous, relevant examples, with an emphasis on the construction, evaluation, and interpretation of mathematical models throughout. Encountering these concepts in context, students learn not only quantitative techniques, but how to bridge between biological and mathematical ways of thinking. Examples range broadly, exploring the dynamics of neurons and the immune system, through to population dynamics and the Google PageRank algorithm. Each scenario relies only on an interest in the natural world; no biological expertise is assumed of student or instructor. Building on a single prerequisite of Precalculus, the book suits a two-quarter sequence for first or second year undergraduates, and meets the mathematical requirements of medical school entry. The later material provides opportunities for more advanced students in both mathematics and life sciences to revisit theoretical knowledge in a rich, real-world framework. In all cases, the focus is clear: how does the math help us understand the science? |
| computer science ucla requirements: Learning to Solve Problems by Searching for Macro-operators Richard E. Korf, 1985 This monograph explores the idea of learning efficient strategies for solving problems by searching for macro-operators. |
| computer science ucla requirements: Power to the Transfer Dimpal Jain, Santiago N. Bernal Melendez, Alfred R. Herrera, 2020-02-01 Currently, U.S. community colleges serve nearly half of all students of color in higher education who, for a multitude of reasons, do not continue their education by transferring to a university. For those students who do transfer, often the responsibility for the application process, retention, graduation, and overall success is placed on them rather than their respective institutions. This book aims to provide direction toward the development and maintenance of a transfer receptive culture, which is defined as an institutional commitment by a university to support transfer students of color. A transfer receptive culture explicitly acknowledges the roles of race and racism in the vertical transfer process from a community college to a university and unapologetically centers transfer as a form of equity in the higher education pipeline. The framework is guided by critical race theory in education, which acknowledges the role of white supremacy and its contemporary and historical role in shaping institutions of higher learning. |
| computer science ucla requirements: Right College, Right Price Frank Palmasani, 2013 Describes how the Financial Fit program can help families determine how much college will really cost beyond the sticker price and factor cost into the college search, and explains how to maximize financial aid benefits. |
| computer science ucla requirements: Modern Compiler Implementation in Java Andrew W. Appel, Jens Palsberg, 2007 Appel explains all phases of a modern compiler, covering current techniques in code generation and register allocation as well as functional and object-oriented languages. The book also includes a compiler implementation project using Java. |
| computer science ucla requirements: Unlocking the Clubhouse Jane Margolis, Allan Fisher, 2003-02-28 Understanding and overcoming the gender gap in computer science education. The information technology revolution is transforming almost every aspect of society, but girls and women are largely out of the loop. Although women surf the Web in equal numbers to men and make a majority of online purchases, few are involved in the design and creation of new technology. It is mostly men whose perspectives and priorities inform the development of computing innovations and who reap the lion's share of the financial rewards. As only a small fraction of high school and college computer science students are female, the field is likely to remain a male clubhouse, absent major changes. In Unlocking the Clubhouse, social scientist Jane Margolis and computer scientist and educator Allan Fisher examine the many influences contributing to the gender gap in computing. The book is based on interviews with more than 100 computer science students of both sexes from Carnegie Mellon University, a major center of computer science research, over a period of four years, as well as classroom observations and conversations with hundreds of college and high school faculty. The interviews capture the dynamic details of the female computing experience, from the family computer kept in a brother's bedroom to women's feelings of alienation in college computing classes. The authors investigate the familial, educational, and institutional origins of the computing gender gap. They also describe educational reforms that have made a dramatic difference at Carnegie Mellon—where the percentage of women entering the School of Computer Science rose from 7% in 1995 to 42% in 2000—and at high schools around the country. |
| computer science ucla requirements: Machine Learning Kevin P. Murphy, 2012-08-24 A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students. |
| computer science ucla requirements: Computational Imaging Ayush Bhandari, Achuta Kadambi, Ramesh Raskar, 2022-10-25 A comprehensive and up-to-date textbook and reference for computational imaging, which combines vision, graphics, signal processing, and optics. Computational imaging involves the joint design of imaging hardware and computer algorithms to create novel imaging systems with unprecedented capabilities. In recent years such capabilities include cameras that operate at a trillion frames per second, microscopes that can see small viruses long thought to be optically irresolvable, and telescopes that capture images of black holes. This text offers a comprehensive and up-to-date introduction to this rapidly growing field, a convergence of vision, graphics, signal processing, and optics. It can be used as an instructional resource for computer imaging courses and as a reference for professionals. It covers the fundamentals of the field, current research and applications, and light transport techniques. The text first presents an imaging toolkit, including optics, image sensors, and illumination, and a computational toolkit, introducing modeling, mathematical tools, model-based inversion, data-driven inversion techniques, and hybrid inversion techniques. It then examines different modalities of light, focusing on the plenoptic function, which describes degrees of freedom of a light ray. Finally, the text outlines light transport techniques, describing imaging systems that obtain micron-scale 3D shape or optimize for noise-free imaging, optical computing, and non-line-of-sight imaging. Throughout, it discusses the use of computational imaging methods in a range of application areas, including smart phone photography, autonomous driving, and medical imaging. End-of-chapter exercises help put the material in context. |
| computer science ucla requirements: Modeling and Reasoning with Bayesian Networks Adnan Darwiche, 2009-04-06 This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer. |
| computer science ucla requirements: Dynamic Systems Biology Modeling and Simulation Joseph DiStefano III, 2015-01-10 Dynamic Systems Biology Modeling and Simuation consolidates and unifies classical and contemporary multiscale methodologies for mathematical modeling and computer simulation of dynamic biological systems – from molecular/cellular, organ-system, on up to population levels. The book pedagogy is developed as a well-annotated, systematic tutorial – with clearly spelled-out and unified nomenclature – derived from the author's own modeling efforts, publications and teaching over half a century. Ambiguities in some concepts and tools are clarified and others are rendered more accessible and practical. The latter include novel qualitative theory and methodologies for recognizing dynamical signatures in data using structural (multicompartmental and network) models and graph theory; and analyzing structural and measurement (data) models for quantification feasibility. The level is basic-to-intermediate, with much emphasis on biomodeling from real biodata, for use in real applications. - Introductory coverage of core mathematical concepts such as linear and nonlinear differential and difference equations, Laplace transforms, linear algebra, probability, statistics and stochastics topics - The pertinent biology, biochemistry, biophysics or pharmacology for modeling are provided, to support understanding the amalgam of math modeling with life sciences - Strong emphasis on quantifying as well as building and analyzing biomodels: includes methodology and computational tools for parameter identifiability and sensitivity analysis; parameter estimation from real data; model distinguishability and simplification; and practical bioexperiment design and optimization - Companion website provides solutions and program code for examples and exercises using Matlab, Simulink, VisSim, SimBiology, SAAMII, AMIGO, Copasi and SBML-coded models - A full set of PowerPoint slides are available from the author for teaching from his textbook. He uses them to teach a 10 week quarter upper division course at UCLA, which meets twice a week, so there are 20 lectures. They can easily be augmented or stretched for a 15 week semester course - Importantly, the slides are editable, so they can be readily adapted to a lecturer's personal style and course content needs. The lectures are based on excerpts from 12 of the first 13 chapters of DSBMS. They are designed to highlight the key course material, as a study guide and structure for students following the full text content - The complete PowerPoint slide package (~25 MB) can be obtained by instructors (or prospective instructors) by emailing the author directly, at: joed@cs.ucla.edu |
| computer science ucla requirements: Algorithms of Oppression Safiya Umoja Noble, 2018-02-20 Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author |
| computer science ucla requirements: Absolute C++ Walter J. Savitch, Kenrick Mock, 2013 Presents the C++ computer programming language. It provides the tools necessary for experienced and novice programmers to master C++, including: thorough coverage of the Standard Template Library; complete and fully executable code throughout; sections highlighting programming tips and common pitfalls; and a logical order of coverage of C++ topics in order for students to better understand the language. C++ is a general-purpose computer programming language. It has imperative, object-oriented and generic programming features, while also providing facilities for low-level memory manipulation |
| computer science ucla requirements: Artificial Intelligence David L. Poole, Alan K. Mackworth, 2017-09-25 Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains. |
| computer science ucla requirements: Introduction to Algorithms Udi Manber, 1989 This book emphasizes the creative aspects of algorithm design by examining steps used in the process of algorithm development. The heart of the creative process lies in an analogy between proving mathematical theorems by induction and designing combinatorial algorithms. The book contains hundreds of problems and examples. It is designed to enhance the reader's problem-solving abilities and understanding of the principles behind algorithm design. 0201120372B04062001 |
| computer science ucla requirements: CS for All Christine Alvarado, Ran Libeskind-Hadas, Geoffrey H. Kuenning, Zachary Dodds, 2019 Provides an introduction to computer science with an emphasis on concepts and problem-solving over syntax and programming language features-- |
| computer science ucla requirements: JavaScript David Flanagan, 2002 A guide for experienced programmers demonstrates the core JavaScript language, offers examples of common tasks, and contains an extensive reference to JavaScript commands, objects, methods, and properties. |
| computer science ucla requirements: Database System Concepts Abraham Silberschatz, Henry F. Korth, S. Sudarshan, 2006 Intended for a first course in databases at junior or senior undergraduate, or first year graduate level, this book provides extensive coverage of concepts, database system internals and tools and techniques. |
| computer science ucla requirements: Bayesian Networks and Decision Graphs Thomas Dyhre Nielsen, FINN VERNER JENSEN, 2009-03-17 This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems. |
| computer science ucla requirements: Dependability: Basic Concepts and Terminology Jean-Claude Laprie, 2013-12-28 |
| computer science ucla requirements: Artificial Intelligence Stuart Russell, Peter Norvig, 2016-09-10 Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. |
| computer science ucla requirements: Foundations of Security Christoph Kern, Anita Kesavan, Neil Daswani, 2007-05-11 Software developers need to worry about security as never before. They need clear guidance on safe coding practices, and that’s exactly what this book delivers. The book does not delve deep into theory, or rant about the politics of security. Instead, it clearly and simply lays out the most common threats that programmers need to defend against. It then shows programmers how to make their defense. The book takes a broad focus, ranging over SQL injection, worms and buffer overflows, password security, and more. It sets programmers on the path towards successfully defending against the entire gamut of security threats that they might face. |
| computer science ucla requirements: Statistical Relational Artificial Intelligence Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole, 2016-03-24 An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks. |
| computer science ucla requirements: Radical Equations Robert Moses, Charles E. Cobb, 2002-06-10 The remarkable story of the Algebra Project, a community-based effort to develop math-science literacy in disadvantaged schools—as told by the program’s founder “Bob Moses was a hero of mine. His quiet confidence helped shape the civil rights movement, and he inspired generations of young people looking to make a difference”—Barack Obama At a time when popular solutions to the educational plight of poor children of color are imposed from the outside—national standards, high-stakes tests, charismatic individual saviors—the acclaimed Algebra Project and its founder, Robert Moses, offer a vision of school reform based in the power of communities. Begun in 1982, the Algebra Project is transforming math education in twenty-five cities. Founded on the belief that math-science literacy is a prerequisite for full citizenship in society, the Project works with entire communities—parents, teachers, and especially students—to create a culture of literacy around algebra, a crucial stepping-stone to college math and opportunity. Telling the story of this remarkable program, Robert Moses draws on lessons from the 1960s Southern voter registration he famously helped organize: “Everyone said sharecroppers didn't want to vote. It wasn't until we got them demanding to vote that we got attention. Today, when kids are falling wholesale through the cracks, people say they don't want to learn. We have to get the kids themselves to demand what everyone says they don't want.” We see the Algebra Project organizing community by community. Older kids serve as coaches for younger students and build a self-sustained tradition of leadership. Teachers use innovative techniques. And we see the remarkable success stories of schools like the predominately poor Hart School in Bessemer, Alabama, which outscored the city's middle-class flagship school in just three years. Radical Equations provides a model for anyone looking for a community-based solution to the problems of our disadvantaged schools. |
| computer science ucla requirements: Physics 4al Grossmann, 2001 |
| computer science ucla requirements: Monte Carlo Methods Adrian Barbu, Song-Chun Zhu, 2020-02-24 This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research. |
| computer science ucla requirements: Cheating Tim Groseclose, 2014-04 Because of California's Proposition 209, public universities such as UCLA cannot use race as a factor in admissions. However, as this book shows, UCLA gives significant preferences to African Americans, while it discriminates against Asians. The author, a professor of political science and economics at UCLA, documents what he witnessed as a member of UCLA's faculty oversight committee for admissions. He also describes findings from a UCLA internal report as well as statistics from a large data set that he has posted online. All show that UCLA is breaking the law. The discrimination is not simply a byproduct of class-based preferences. For instance, for one aspect of the admissions process, a rich African American's chance of admission is almost double that of a poor Asian, even when the two applicants have identical grades, SAT scores, and other factors. |
| computer science ucla requirements: The Florentine Deception Carey Nachenberg, 2015-09-01 In this action-packed techno-thriller, a routine computer cleanup sets off an electrifying quest for an enigmatic—and deadly—treasure. After selling his dorm-room startup for millions and effectively retiring at the age of twenty-five, Alex Fife is eager for a new challenge. When he agrees to clean up an old PC as a favor, he never expects to find the adventure of a lifetime waiting for him inside the machine. But as he rummages through old emails, Alex stumbles upon a startling discovery: The previous owner, a shady antiques smuggler, had been trying to unload a mysterious object known as the Florentine on the black market. And with the dealer’s untimely passing, the Florentine is now unaccounted for and ripe for the taking. Alex dives headfirst into a hunt for the priceless object. What starts out as a seemingly innocuous pursuit quickly devolves into a nightmare when Alex discovers the true technological nature of the Florentine. Not just a lost treasure, it’s something far more insidious: a weapon that could bring the developed world to its knees. Alex races through subterranean grottos, freezing morgues, and hidden cellars in the dark underbelly of Los Angeles, desperate to find the Florentine before it falls into the wrong hands. Because if nefarious forces find it first, there’ll be nothing Alex—or anyone else—can do to prevent a catastrophic attack. Leading security specialist Carey Nachenberg delivers expert technical details in this gripping, highly entertaining cyber thrill ride—perfect for fans of Neal Stephenson and William Gibson. |
| computer science ucla requirements: Beyond the Valley Ramesh Srinivasan, 2020-09-01 How to repair the disconnect between designers and users, producers and consumers, and tech elites and the rest of us: toward a more democratic internet. In this provocative book, Ramesh Srinivasan describes the internet as both an enabler of frictionless efficiency and a dirty tangle of politics, economics, and other inefficient, inharmonious human activities. We may love the immediacy of Google search results, the convenience of buying from Amazon, and the elegance and power of our Apple devices, but it's a one-way, top-down process. We're not asked for our input, or our opinions—only for our data. The internet is brought to us by wealthy technologists in Silicon Valley and China. It's time, Srinivasan argues, that we think in terms beyond the Valley. Srinivasan focuses on the disconnection he sees between designers and users, producers and consumers, and tech elites and the rest of us. The recent Cambridge Analytica and Russian misinformation scandals exemplify the imbalance of a digital world that puts profits before inclusivity and democracy. In search of a more democratic internet, Srinivasan takes us to the mountains of Oaxaca, East and West Africa, China, Scandinavia, North America, and elsewhere, visiting the “design labs” of rural, low-income, and indigenous people around the world. He talks to a range of high-profile public figures—including Elizabeth Warren, David Axelrod, Eric Holder, Noam Chomsky, Lawrence Lessig, and the founders of Reddit, as well as community organizers, labor leaders, and human rights activists.. To make a better internet, Srinivasan says, we need a new ethic of diversity, openness, and inclusivity, empowering those now excluded from decisions about how technologies are designed, who profits from them, and who are surveilled and exploited by them. |
| computer science ucla requirements: Communication Nets Leonard Kleinrock, 2014-06-10 Considerable research has been devoted to the formulation and solution of problems involving flow within connected networks. Independent of these surveys, an extensive body of knowledge has accumulated on the subject of queues, particularly in regard to stochastic flow through single-node servicing facilities. This text combines studies of connected networks with those of stochastic flow, providing a basis for understanding the general behavior and operation of communication networks in realistic situations. Author Leonard Kleinrock of the Computer Science Department at UCLA created the basic principle of packet switching, the technology underpinning the Internet. In this text, he develops a queuing theory model of communications nets. Its networks are channel-capacity limited; consequently, the measure of performance is taken to be the average delay encountered by a message in passing through the net. Topics include questions pertaining to optimal channel capacity assignment, effect of priority and other queue disciplines, choice of routine procedure, fixed-cost restraint, and design of topological structures. Many separate facets are brought into focus in the concluding discussion of the simulation of communication nets, and six appendices offer valuable supplementary information. |
| computer science ucla requirements: Systems and Computer Science John F Hart, Satoru Takasu, 1967-12-15 This book presents the papers delivered at the Conference on Systems and Computer Science held at the University of Western Ontario in September 1965. The primary purposes of the Conference were the promotion of research and the development of the teaching of computer science in Canadian universities. The papers focus attention on some of the concepts of Computer Science as a new field of study and at the same time provide a background for scientists looking at the subject for the first time. The chief developments in computer science have been concerned with the applied rather than the pure areas of the field: numerical analysis, applied statistics and operations research, and data processing. But there is something more to computers than the physical components and this book represents an attempt to correct the imbalance between applied and pure by drawing attention to certain theoretical aspects of computer and information science. Among the topics discussed are the theory of finite and infinite automata, aspects of formal language theory, heuristic and non-heuristic approaches to theorem proving and the mathematical formulation of the theory of general systems. There are also references to the problems of machine design, to software systems including higher-level languages, to multiple control computer models and to applied systems. This collection of papers will appeal first to graduate students and professors in Computer Science. It will also be of interest to computer scientists in industry and in government and university research groups and to the scientific public interested in discovering some of the principal ingredients and directions of the computer and information sciences. |
| computer science ucla requirements: Mathematics for Economists Carl P. Simon, Lawrence Blume, 1994 Mathematics for Economists, a new text for advanced undergraduate and beginning graduate students in economics, is a thoroughly modern treatment of the mathematics that underlies economic theory. An abundance of applications to current economic analysis, illustrative diagrams, thought-provoking exercises, careful proofs, and a flexible organisation-these are the advantages that Mathematics for Economists brings to today's classroom. |
| computer science ucla requirements: Building Problem Solvers Kenneth D. Forbus, Johan De Kleer, 1993 After working through Building Problem Solvers, readers should have a deep understanding of pattern directed inference systems, constraint languages, and truth maintenance systems. |
| computer science ucla requirements: Introduction to Mathematical Thinking Keith J. Devlin, 2012 Mathematical thinking is not the same as 'doing math'--unless you are a professional mathematician. For most people, 'doing math' means the application of procedures and symbolic manipulations. Mathematical thinking, in contrast, is what the name reflects, a way of thinking about things in the world that humans have developed over three thousand years. It does not have to be about mathematics at all, which means that many people can benefit from learning this powerful way of thinking, not just mathematicians and scientists.--Back cover. |
| computer science ucla requirements: Dynamic Biosystem Modeling & Simulation Methodology - Integrated & Accessible Joseph Distefano, 3rd, 2019-09-16 This textbook is uniquely crafted for use in teaching undergraduate students in the life, math, computer and other sciences and engineering. It is INTRODUCTORY LEVEL, for students who have taken or are currently completing their undergraduate math requirements, and are acquiring analytical-thinking and doing skills, along with introductory biology, chemistry and physics subject matter. It's about learning HOW to model and simulate dynamic biological systems, which also makes it useful for graduate students and professional researchers who want a more rigorous treatment of introductory life science math modeling, integrated with the biology. It brings together the multidisciplinary pedagogy of these subjects into a SINGLE INTRODUCTORY MODELING METHODOLOGY COURSE, crystalizing the experience of an author who has been teaching dynamic biosystems modeling and simulation methodology for the life sciences for more than 50 years. DiStefano maximizes accessibility and systems-math-biology integration - without diminishing conceptual rigor. Minimally essential applied math and SYSTEMS ENGINEERING METHODS are included, along with a synopsis of the biology and physiology underlying dynamic biosystem modeling, all in a modeling pedagogy context. This textbook fills a major need in the training of contemporary biology students.Dynamic biosystems modeling methodology is presented over 12 distinctive chapters, primarily with systems diagrams and simple differential equations and algebra for expressing them quantitatively, integrated with the biology. Solving and analyzing (quantifying) the biomodels are then accomplished by simulation, using a facile control system simulation language Simulink, a GUI/Matlab toolbox that emulates control systems diagramming, rather than by coding the model in a standard computer programming language. Students see and work with the system model - not the code - a big plus. Higher math and complex analytical solutions are avoided.Each chapter begins with a list of LEARNING GOALS, to help with both perspective for the chapter material, and retrospective, to measure learning. EXERCISES for the student at the end of each chapter are designed to test and reinforce learning. A SOLUTIONS MANUAL for chapter exercises is available to qualified instructors from the author, as are LECTURE SLIDES and LAB ASSIGNMENTS AND SOLUTIONS, for courses that adopt the textbook for student use. |
| computer science ucla requirements: Structure and Interpretation of Computer Programs Harold Abelson, Gerald Jay Sussman, 2022-05-03 A new version of the classic and widely used text adapted for the JavaScript programming language. Since the publication of its first edition in 1984 and its second edition in 1996, Structure and Interpretation of Computer Programs (SICP) has influenced computer science curricula around the world. Widely adopted as a textbook, the book has its origins in a popular entry-level computer science course taught by Harold Abelson and Gerald Jay Sussman at MIT. SICP introduces the reader to central ideas of computation by establishing a series of mental models for computation. Earlier editions used the programming language Scheme in their program examples. This new version of the second edition has been adapted for JavaScript. The first three chapters of SICP cover programming concepts that are common to all modern high-level programming languages. Chapters four and five, which used Scheme to formulate language processors for Scheme, required significant revision. Chapter four offers new material, in particular an introduction to the notion of program parsing. The evaluator and compiler in chapter five introduce a subtle stack discipline to support return statements (a prominent feature of statement-oriented languages) without sacrificing tail recursion. The JavaScript programs included in the book run in any implementation of the language that complies with the ECMAScript 2020 specification, using the JavaScript package sicp provided by the MIT Press website. |
| computer science ucla requirements: The Practice of Programming Brian W. Kernighan, Rob Pike, 1999-02-09 With the same insight and authority that made their book The Unix Programming Environment a classic, Brian Kernighan and Rob Pike have written The Practice of Programming to help make individual programmers more effective and productive. The practice of programming is more than just writing code. Programmers must also assess tradeoffs, choose among design alternatives, debug and test, improve performance, and maintain software written by themselves and others. At the same time, they must be concerned with issues like compatibility, robustness, and reliability, while meeting specifications. The Practice of Programming covers all these topics, and more. This book is full of practical advice and real-world examples in C, C++, Java, and a variety of special-purpose languages. It includes chapters on: debugging: finding bugs quickly and methodically testing: guaranteeing that software works correctly and reliably performance: making programs faster and more compact portability: ensuring that programs run everywhere without change design: balancing goals and constraints to decide which algorithms and data structures are best interfaces: using abstraction and information hiding to control the interactions between components style: writing code that works well and is a pleasure to read notation: choosing languages and tools that let the machine do more of the work Kernighan and Pike have distilled years of experience writing programs, teaching, and working with other programmers to create this book. Anyone who writes software will profit from the principles and guidance in The Practice of Programming. |
| computer science ucla requirements: Fundamentals of Artificial Intelligence K.R. Chowdhary, 2020-04-04 Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI. |
| computer science ucla requirements: System-Level Synthesis Ahmed Amine Jerraya, Jean Mermet, 2012-12-06 System-Level Synthesis deals with the concurrent design of electronic applications, including both hardware and software. The issue has become the bottleneck in the design of electronic systems, including both hardware and software, in several major industrial fields, including telecommunications, automotive and aerospace engineering. The major difficulty with the subject is that it demands contributions from several research fields, including system specification, system architecture, hardware design, and software design. Most existing book cover well only a few aspects of system-level synthesis. The present volume presents a comprehensive discussion of all the aspects of system-level synthesis. Each topic is covered by a contribution written by an international authority on the subject. |
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Computer | Definition, History, Operating Systems, & Facts
A computer is a programmable device for processing, storing, and displaying information. Learn more in this article about modern digital electronic computers and their design, constituent parts, …
What is a Computer?
Feb 6, 2025 · What is a Computer? A computer is a programmable device that stores, retrieves, and processes data. The term "computer" was originally given to humans (human computers) who …
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What is a Computer? - GeeksforGeeks
Apr 7, 2025 · A computer is an electronic device that processes, stores, and executes instructions to perform tasks. It includes key components such as the CPU (Central Processing Unit), RAM …
Computer Basics: What is a Computer? - GCFGlobal.org
What is a computer? A computer is an electronic device that manipulates information, or data. It has the ability to store, retrieve, and process data. You may already know that you can use a …
What is a Computer? (Definition & Meaning) - Webopedia
Oct 9, 2024 · A computer is a programmable machine that responds to specific instructions and uses hardware and software to perform tasks. Different types of computers, including desktops, …
Computer - Simple English Wikipedia, the free encyclopedia
A computer is a machine that uses electronics to input, process, store, and output data. Data is information such as numbers, words, and lists. Input of data means to read information from a …
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What is Computer? Definition, Characteristics and Classification
Aug 7, 2024 · A computer is an electronic device wherein we need to input raw data to be processed with a set of programs to produce a desirable output. Computers have the ability to …
