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| caltech data science masters: The Mathematics of Data Michael W. Mahoney, John C. Duchi, Anna C. Gilbert, 2018-11-15 Nothing provided |
| caltech data science masters: Adviser, Teacher, Role Model, Friend National Academy of Engineering, National Academy of Sciences, Policy and Global Affairs, Institute of Medicine, Committee on Science, Engineering, and Public Policy, 1997-08-30 This guide offers helpful advice on how teachers, administrators, and career advisers in science and engineering can become better mentors to their students. It starts with the premise that a successful mentor guides students in a variety of ways: by helping them get the most from their educational experience, by introducing them to and making them comfortable with a specific disciplinary culture, and by offering assistance with the search for suitable employment. Other topics covered in the guide include career planning, time management, writing development, and responsible scientific conduct. Also included is a valuable list of bibliographical and Internet resources on mentoring and related topics. |
| caltech data science masters: Strength in Numbers: The Rising of Academic Statistics Departments in the U. S. Alan Agresti, Xiao-Li Meng, 2012-11-02 Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department’s history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history. |
| caltech data science masters: Escape from Earth Fraser MacDonald, 2019-06-25 The long-buried truth about the dawn of the Space Age: lies, spies, socialism, and sex magick. Los Angeles, 1930s: Everyone knows that rockets are just toys, the stuff of cranks and pulp magazines. Nevertheless, an earnest engineering student named Frank Malina sets out to prove the doubters wrong. With the help of his friend Jack Parsons, a grandiose and occult-obsessed explosives enthusiast, Malina embarks on a journey that takes him from junk yards and desert lots to the heights of the military-industrial complex. Malina designs the first American rocket to reach space and establishes the Jet Propulsion Laboratory. But trouble soon finds him: the FBI suspects Malina of being a communist. And when some classified documents go missing, will his comrades prove as dependable as his engineering? Drawing on an astonishing array of untapped sources, including FBI documents and private archives, Escape From Earth tells the inspiring true story of Malina's achievements--and the political fear that's kept them hidden. At its heart, this is an Icarus tale: a real life fable about the miracle of human ingenuity and the frailty of dreams. |
| caltech data science masters: Third Caltech Conference on Very Large Scale Integration R. Bryant, 2012-12-06 The papers in this book were presented at the Third Caltech Conference on Very Large Scale Integration, held March 21-23, 1983 in Pasadena, California. The conference was organized by the Computer Science Depart ment, California Institute of Technology, and was partly supported by the Caltech Silicon Structures Project. This conference focused on the role of systematic methodologies, theoretical models, and algorithms in all phases of the design, verification, and testing of very large scale integrated circuits. The need for such disciplines has arisen as a result of the rapid progress of integrated circuit technology over the past 10 years. This progress has been driven largely by the fabrica tion technology, providing the capability to manufacture very complex elec tronic systems reliably and at low cost. At this point the capability to manufac ture very large scale integrated circuits has exceeded our capability to develop new product designs quickly, reliably, and at a reasonable cost. As a result new designs are undertaken only if the production volume will be large enough to amortize high design costs, products first appear on the market well past their announced delivery date, and reference manuals must be amended to document design flaws. Recent research in universities and in private industry has created an emerg ing science of very large scale integration. |
| caltech data science masters: High-Dimensional Probability Roman Vershynin, 2018-09-27 An integrated package of powerful probabilistic tools and key applications in modern mathematical data science. |
| caltech data science masters: Physical Foundations of Cosmology Viatcheslav Mukhanov, 2005-11-10 Inflationary cosmology has been developed over the last twenty years to remedy serious shortcomings in the standard hot big bang model of the universe. This textbook, first published in 2005, explains the basis of modern cosmology and shows where the theoretical results come from. The book is divided into two parts; the first deals with the homogeneous and isotropic model of the Universe, the second part discusses how inhomogeneities can explain its structure. Established material such as the inflation and quantum cosmological perturbation are presented in great detail, however the reader is brought to the frontiers of current cosmological research by the discussion of more speculative ideas. An ideal textbook for both advanced students of physics and astrophysics, all of the necessary background material is included in every chapter and no prior knowledge of general relativity and quantum field theory is assumed. |
| caltech data science masters: A Mathematical Introduction to Fluid Mechanics A. J. Chorin, J. E. Marsden, 2012-12-06 These notes are based on a one-quarter (i. e. very short) course in fluid mechanics taught in the Department of Mathematics of the University of California, Berkeley during the Spring of 1978. The goal of the course was not to provide an exhaustive account of fluid mechanics, nor to assess the engineering value of various approxima tion procedures. The goals were: (i) to present some of the basic ideas of fluid mechanics in a mathematically attractive manner (which does not mean fully rigorous); (ii) to present the physical back ground and motivation for some constructions which have been used in recent mathematical and numerical work on the Navier-Stokes equations and on hyperbolic systems; (iil. ) 'to interest some of the students in this beautiful and difficult subject. The notes are divided into three chapters. The first chapter contains an elementary derivation of the equations; the concept of vorticity is introduced at an early stage. The second chapter contains a discussion of potential flow, vortex motion, and boundary layers. A construction of boundary layers using vortex sheets and random walks is presented; it is hoped that it helps to clarify the ideas. The third chapter contains an analysis of one-dimensional gas iv flow, from a mildly modern point of view. Weak solutions, Riemann problems, Glimm's scheme, and combustion waves are discussed. The style is informal and no attempt was made to hide the authors' biases and interests. |
| caltech data science masters: The Fundamentals of Heavy Tails Jayakrishnan Nair, Adam Wierman, Bert Zwart, 2022-06-09 Heavy tails –extreme events or values more common than expected –emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks the natural emergence of heavy-tailed distributions from a wide variety of general processes, building intuition. And it reveals the controversy surrounding heavy tails to be the result of flawed statistics, then equips readers to identify and estimate with confidence. Over 100 exercises complete this engaging package. |
| caltech data science masters: Dr. E's Super Stellar Solar System Bethany Ehlmann, Jennifer Swanson, 2018 This stellar book introduces kids to outer space through in-depth info and comic book adventure. Along the way, kids follow explorer Bethany Ehlmann, a member of the NASA Mars Rover Curiosity mission, and her lovable robo-dog, Rover, as they study and protect our amazing solar system. |
| caltech data science masters: Decision Neuroscience Jean-Claude Dreher, Léon Tremblay, 2016-09-27 Decision Neuroscience addresses fundamental questions about how the brain makes perceptual, value-based, and more complex decisions in non-social and social contexts. This book presents compelling neuroimaging, electrophysiological, lesional, and neurocomputational models in combination with hormonal and genetic approaches, which have led to a clearer understanding of the neural mechanisms behind how the brain makes decisions. The five parts of the book address distinct but inter-related topics and are designed to serve both as classroom introductions to major subareas in decision neuroscience and as advanced syntheses of all that has been accomplished in the last decade. Part I is devoted to anatomical, neurophysiological, pharmacological, and optogenetics animal studies on reinforcement-guided decision making, such as the representation of instructions, expectations, and outcomes; the updating of action values; and the evaluation process guiding choices between prospective rewards. Part II covers the topic of the neural representations of motivation, perceptual decision making, and value-based decision making in humans, combining neurcomputational models and brain imaging studies. Part III focuses on the rapidly developing field of social decision neuroscience, integrating recent mechanistic understanding of social decisions in both non-human primates and humans. Part IV covers clinical aspects involving disorders of decision making that link together basic research areas including systems, cognitive, and clinical neuroscience; this part examines dysfunctions of decision making in neurological and psychiatric disorders, such as Parkinson's disease, schizophrenia, behavioral addictions, and focal brain lesions. Part V focuses on the roles of various hormones (cortisol, oxytocin, ghrelin/leptine) and genes that underlie inter-individual differences observed with stress, food choices, and social decision-making processes. The volume is essential reading for anyone interested in decision making neuroscience. With contributions that are forward-looking assessments of the current and future issues faced by researchers, Decision Neuroscience is essential reading for anyone interested in decision-making neuroscience. - Provides comprehensive coverage of approaches to studying individual and social decision neuroscience, including primate neurophysiology, brain imaging in healthy humans and in various disorders, and genetic and hormonal influences on decision making - Covers multiple levels of analysis, from molecular mechanisms to neural-systems dynamics and computational models of how we make choices - Discusses clinical implications of process dysfunctions, including schizophrenia, Parkinson's disease, eating disorders, drug addiction, and pathological gambling - Features chapters from top international researchers in the field and full-color presentation throughout with numerous illustrations to highlight key concepts |
| caltech data science masters: "Surely You're Joking, Mr. Feynman!": Adventures of a Curious Character Richard P. Feynman, 2018-02-06 One of the most famous science books of our time, the phenomenal national bestseller that buzzes with energy, anecdote and life. It almost makes you want to become a physicist (Science Digest). Richard P. Feynman, winner of the Nobel Prize in physics, thrived on outrageous adventures. In this lively work that “can shatter the stereotype of the stuffy scientist” (Detroit Free Press), Feynman recounts his experiences trading ideas on atomic physics with Einstein and cracking the uncrackable safes guarding the most deeply held nuclear secrets—and much more of an eyebrow-raising nature. In his stories, Feynman’s life shines through in all its eccentric glory—a combustible mixture of high intelligence, unlimited curiosity, and raging chutzpah. Included for this edition is a new introduction by Bill Gates. |
| caltech data science masters: GMAT Foundations of Verbal Manhattan Prep, 2020-01-07 Developed for test-takers who need a refresher, Manhattan Prep's GMAT Foundations of Verbal provides a user-friendly review of basic verbal concepts crucial for GMAT success. Written by active instructors with 99th-percentile scores, GMAT Foundations of Verbal is designed to help students, particularly ESL students, who struggle with the basics of the verbal section of the GMAT. The book comes with robust online resources, including a practice test, a question bank and interactive lessons. Designed to be user-friendly for all students, GMAT Foundations of Verbal provides: Review of foundational grammar such as parts of speech and sentence structure Strategies for tackling the three verbal question types—Sentence Correction, Critical Reasoning, and Reading Comprehension Easy-to-follow examples and comprehensive explanations GMAT Foundations of Verbal is an invaluable resource for any student who wants to cement their understanding and build their basic verbal skills for the GMAT. |
| caltech data science masters: A First Course in Partial Differential Equations H. F. Weinberger, 2012-04-20 Suitable for advanced undergraduate and graduate students, this text presents the general properties of partial differential equations, including the elementary theory of complex variables. Solutions. 1965 edition. |
| caltech data science masters: Statistical Regression and Classification Norman Matloff, 2017-09-19 Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA. |
| caltech data science masters: Learning from Data Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin, 2012-01-01 |
| caltech data science masters: A Course in Mathematical Analysis Edouard Goursat, Earle Raymond Hedrick, Otto Dunkel, 2017-09-16 Trieste Publishing has a massive catalogue of classic book titles. Our aim is to provide readers with the highest quality reproductions of fiction and non-fiction literature that has stood the test of time. The many thousands of books in our collection have been sourced from libraries and private collections around the world.The titles that Trieste Publishing has chosen to be part of the collection have been scanned to simulate the original. Our readers see the books the same way that their first readers did decades or a hundred or more years ago. Books from that period are often spoiled by imperfections that did not exist in the original. Imperfections could be in the form of blurred text, photographs, or missing pages. It is highly unlikely that this would occur with one of our books. Our extensive quality control ensures that the readers of Trieste Publishing's books will be delighted with their purchase. Our staff has thoroughly reviewed every page of all the books in the collection, repairing, or if necessary, rejecting titles that are not of the highest quality. This process ensures that the reader of one of Trieste Publishing's titles receives a volume that faithfully reproduces the original, and to the maximum degree possible, gives them the experience of owning the original work.We pride ourselves on not only creating a pathway to an extensive reservoir of books of the finest quality, but also providing value to every one of our readers. Generally, Trieste books are purchased singly - on demand, however they may also be purchased in bulk. Readers interested in bulk purchases are invited to contact us directly to enquire about our tailored bulk rates. |
| caltech data science masters: Demystifying AI for the Enterprise Prashant Natarajan, Bob Rogers, Edward Dixon, Jonas Christensen, Kirk Borne, Leland Wilkinson, Shantha Mohan, 2021-12-30 Artificial intelligence (AI) in its various forms –– machine learning, chatbots, robots, agents, etc. –– is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets. With increasing business process and workflow maturity, coupled with recent trends in cloud computing, datafication, IoT, cybersecurity, and advanced analytics, there is an understanding that the challenges of tomorrow cannot be solely addressed by today’s people, processes, and products. There is still considerable mystery, hype, and fear about AI in today’s world. A considerable amount of current discourse focuses on a dystopian future that could adversely affect humanity. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, the current state of business and technology, or the primarily augmentative nature of tomorrow’s AI. This book demystifies AI for the enterprise. It takes readers from the basics (definitions, state-of-the-art, etc.) to a multi-industry journey, and concludes with expert advice on everything an organization must do to succeed. Along the way, we debunk myths, provide practical pointers, and include best practices with applicable vignettes. AI brings to enterprise the capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow’s enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues, and achieve better customer and user satisfaction. |
| caltech data science masters: Astrochemistry: Recent Successes and Current Challenges (IAU S231) International Astronomical Union. Symposium, International Astronomical Union, 2006-04-27 An up-to-date survey of astrochemistry in the early years of the twenty-first century. For researchers and graduate students. |
| caltech data science masters: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| caltech data science masters: Third Caltech Conference on Very Large Scale Integration Randal Bryant, 1983 |
| caltech data science masters: Pragmatic AI Noah Gift, 2018-07-12 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| caltech data science masters: Millikan’s School: A History of the California Institute of Technology Judith Goodstein, 2020-10-19 In November 1891, wealthy former abolitionist and Chicago politician Amos Throop founded a thoroughly undistinguished small college in Pasadena, California, which he named after himself. Millikan’s School is the history of this institution that stands today at the pinnacle of world academics, with 300 full-time faculty, nearly 1,000 undergraduate, 1,250 graduate students and 39 Caltech and alumni Nobel Prize recipients. Although Amos Throop — the name of the college was changed to Caltech in 1920 — could not have realized the importance of geography, the fact that Pasadena lay at the foot of Mount Wilson, was central to its success: astronomer George Ellery Hale built his telescope there in 1902, the finest at that time in the world. Later Hale joined the board of trustees of the struggling school and persuaded Arthur Amos Noyes, former president of MIT and the nation’s leading physical chemist, to join him in Pasadena. The third member of Caltech’s founding troika was renowned physicist Robert A. Millikan from the University of Chicago. The dedication of Caltech in 1920 and the proclamation of what it stood for in science and education set the stage for Millikan, who functioned as the school’s president, to bring the best and the brightest from all over the world — Theodore von Kármán in aeronautics, Thomas Hunt Morgan in biology, Paul Sophus Epstein in physics, Beno Gutenberg in seismology, Linus Pauling in chemistry — to Pasadena to work in an ever larger number of areas in science and technology. The book also covers the funding, planning and construction of the 200-inch telescope on Palomar Mountain, Willy Fowler’s work in nuclear astrophysics and the wartime rocket experiments that grew into the Jet Propulsion Laboratory (JPL), today the world leader in deep-space exploration. “Millikan’s School presents an interesting and thoroughly reliable account of the astonishing change over a period of a few years of a small technical school in Pasadena, California, into one of the world’s leading scientific institutions. “ — Linus Pauling “In Millikan’s School, Judith Goodstein tells the remarkable story of the rise of Caltech... She details how Millikan, aided by Hale and Arthur Amos Noyes, America’s leading physical chemist and another of Hale’s inspired acquisitions, took a former trade school and forged from it a ‘grandiose university among the orange groves’... It would be impossible, while reading Goodstein’s lively account, not to be impressed by the energy, drive and boundless enthusiasm of men like Millikan, Hale and Noyes... [who] had the bare-faced audacity to set about building an institute to rival the cream of the universities of Europe and America.” — Marcus Chown, New Scientist “[Goodstein’s] story is first and foremost the tale of three men: the astronomer George Ellery Hale, the chemist Alfred Noyes, and the physicist Robert Millikan. It is the story of their attempts to transform an undistinguished little school founded in 1891... into a world-class scientific establishment... [A] useful book.” — Tony Rothman, Science “In Millikan’s School, the story of Throop [University]’s transformation into Caltech is told with precision... Judith Goodstein’s history offers a quick tour of the landmarks of science in the mid-20th Century and a glance at how pure science puts itself at the service of government, commerce and the military... Goodstein... approaches her subject with a healthy sense of humor and an acute sense of academic politics. She tells a wonderful story about how Caltech lost to Princeton in a bidding war over the services of Albert Einstein, for example... To her credit, Goodstein asks the hard question: ‘What is the best way to do science?’... Millikan’s School offers enough hard data to enable us to come to our own conclusions.” — Jonathan Kirsch, Los Angeles Times “A cleanly written, scientifically well informed account of one of the world’s foremost institutions for science and technology.” — Ed Regis, Nature “Relying on archival material, published secondary sources, and interviews with institute scientists, Goodstein presents a highly readable account of Caltech’s beginnings at the turn of the century... substantive, informative, and a good read.” — Rebecca S. Lowen, Technology and Culture “As a history of science, this book is well crafted. Orderly in its flow, it is not only a tribute to Millikan, but also places him within the development of physics as a field.” — Andrew Rolle, Southern California Quarterly “A fascinating history that speaks to issues far larger than Cal Tech itself... This well-written and honest account (witness the many cited instances of anti-Semitism in the scientific world) is both a good read and a sobering reminder that big science and top schools are not brought by storks.” — Carroll Pursell, History of Education Quarterly “The author focuses on the personalities and the research fields of the principal scientific figures... The [...] emphasis on personalities, and capsule surveys of relevant scientific fields produce a book that can be apprehended by a wide audience.” — Roger Geiger, Isis “This chronicle offers glimpses of the passion and drive that have motivated a roster of distinguished scientists.” — Publishers Weekly “A lively tale... [Goodstein’s] individual profiles are lean and candid; her background on subjects as diverse as nuclear astrophysics, seismology, aeronautical design, quantum mechanics and rocket fuel are crisp and understandable... With a light style... and meticulous documentation, Goodstein has produced a tale worthy of her subject... “ — Marshall Robinson, Foundation News “A distinguished and uniquely American institution has found its chronicler and its chronicle in Judith Goodstein’s thorough but compact story of Millikan ‘s School. The emergence of Caltech as a powerhouse of science and engineering and a makeweight in the technological advancement of 20th century industry is both beautifully and reliably presented.” — Harry Woolf, Institute for Advanced Study, Princeton University |
| caltech data science masters: Asian Population History Ts'ui-jung Liu, James Lee, David Sven Reher, Osamu Saito, Wang Feng, 2001-05-10 The study of Asian historical demography has lagged behind that of its European and American counterparts for some time. This volume serves to narrow the gap by drawing together material from scholars specializing in demography across the spectrum of Asian countries. The collection divides into four parts and contains nineteen chapters covering issues on comparative perspective, fertility, disease and mortality, and marriage and family. The geographic coverage of the chapters is also wide, extending from East Asia to South Asia, with specific emphasis on Japan, China, Taiwan, Indonesia, India, and Sri Lanka. Authors focus on a whole range of social groups, discussing how demographic issues affect and have affected both urban and rural dwellers from the seventeenth to the twentieth century. This volume, which is perhaps the first to bring together a number of in-depth, specialist studies on Asian population history, should prove a useful and engaging tool for both students and academics in the fields of demography, history, and Asian studies. |
| caltech data science masters: NSSDC Data Listing , 1994 |
| caltech data science masters: Principles of Neurobiology Liqun Luo, 2015-07-14 Principles of Neurobiology presents the major concepts of neuroscience with an emphasis on how we know what we know. The text is organized around a series of key experiments to illustrate how scientific progress is made and helps upper-level undergraduate and graduate students discover the relevant primary literature. Written by a single author in |
| caltech data science masters: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| caltech data science masters: Programming Collective Intelligence Toby Segaran, 2007-08-16 Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details. -- Dan Russell, Google Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths. -- Tim Wolters, CTO, Collective Intellect |
| caltech data science masters: Science and Life Robert Andrews Millikan, 1924 |
| caltech data science masters: Neurotransmitter Transporters Susan G. Amara, 1998-09-15 General Description of the Series: Neurotransmitter Transporters focuses on biochemical, electrophysiological, pharmacological, molecular, and cell biological approaches used to study neurotransmitter transport systems. The articles provide detailed descriptions of procedures that should enable the reader to understand how they are accomplished and to repeat or adapt them for their own experimental needs. This book is the first to focus on methods that have been the basis for the rapid development of this area. General Description of the Series: The critically acclaimed laboratory standard for more than forty years, Methods in Enzymology is one of the most highly respected publications in the field of biochemistry. Since 1955, each volume has been eagerly awaited, frequently consulted, and praised by researchers and reviewers alike. Now with more than 300 volumes (all of them still in print), the series contains much material still relevant today--truly an essential publication for researchers in all fields of life sciences. Key Features * The transport of CNS neurotransmitter transporters * Electrophysiological, biochemical, molecular, cellular biological, pharmacological, neurochemical, and structural approaches * Both plasma and vesicular carriers |
| caltech data science masters: Motion Structures Zhong You, Yan Chen, 2011-08-26 Motion structures are simply assemblies of resistant bodies connected by movable joints. Unlike conventional structures, they allow large shape transformations to satisfy practical requirements and they can be used in:shelters, emergency structures and exhibition standsaircraft morphing wingssatellite solar panels and space antennasmorphing core ma |
| caltech data science masters: GMAT Official Guide Verbal Review 2022 GMAC (Graduate Management Admission Council), 2021-06-16 Add over 340 verbal practice questions to your prep. Designed by the makers of the GMAT™ exam. Your official source of real GMAT questions from past exams. Set yourself up for success with extra practice on the verbal section of the GMAT exam. Study with over 340 practice questions not included in GMAT™ Official Guide 2022: Book & Online Question Bank! Review answer explanations to help improve your performance. GMAT practice questions are organized by difficulty level: easy, medium and hard. Start at the beginning and work your way up to the hard questions as you build upon your knowledge. All practice questions are from past GMAT exams. The GMAT™ Official Guide Verbal Review 2022: Book + Online Question Bank provides 3 ways to study: Book: Know what to expect on the GMAT exam Learn the exam structure with an introductory review chapter followed by 25 practice questions. Review common formulas and concepts using quick reference sheets. Master reading comprehension and critical reasoning with over 340 practice questions from past GMAT exams, organized by difficulty level. GMAT Online Prep Tools: Focus your studying – Bonus: included with purchase! Practice online with the same questions from the book. Create custom practice sets by difficulty level and by fundamental skill. Track your progress using performance metrics. Prepare for exam day by timing your practice in exam mode. Test your knowledge of key concepts with flashcards. Prepare with the Online Question Bank, which includes online-exclusive questions filterable by difficulty level, question type, fundamental skills, and more. Study anytime, anywhere with the Mobile App: review and reattempt practice sets to improve performance in study or exam mode. Mobile App: Your GMAT prep on the go Study offline after downloading the question sets. Sync between devices. Start on your phone, finish on your computer. Add GMAT™ Official Guide Verbal Review 2022: Book + Online Question Bank to your GMAT prep; the official source of practice questions from past GMAT exams. This product includes a print book with a unique access code to the Online Question Bank and Mobile App. |
| caltech data science masters: Physics of Waves William C. Elmore, Mark A. Heald, 2012-04-26 Ideal as a classroom text or for individual study, this unique one-volume overview of classical wave theory covers wave phenomena of acoustics, optics, electromagnetic radiations, and more. |
| caltech data science masters: Advances in Communications D.G. Lainiotis, N.S. Tzannes, 1980-08-31 The technical program of the Conference consisted of technical sessions that covered all-important aspects of control, informa tion processing/communications, and computers. In particular, there were numerous technical sessions covering all-important aspects of control, such as control theory, control applications, estima tion, identification, adaptive systems, linear systems, stability, cybernetics, computational methods, and simulation. The program also included many sessions on information processing/communicati ons, such as information theory, coding, signal analysis, signal processing, communication theory, satellite communications, pat tern recognition and image processing. Moreover, there were sever al sessions on computers, in particular on computer systems, com puter communication networks, and automata. Finally, the technical program included numerous sessions on important applications of systems technology, such as power, energy modeling and planning, earth resources, transportation, economics and management, and physiological systems. In view of the broad scope of the Conference technical program and the extensive coverage of many important aspects of systems theory and applications by internationally known researchers, we hope that this collection of papers will be a useful supplement to the published literature and textbooks used for research and teaching. For the success of the Conference we are indebted to a great many people and institutions, primarily to the authors of the papers, without whom the conference would not have existed, and this book would not have materialized. We are particularly indebted to the great majority of them who paid some or all of their own expenses. |
| caltech data science masters: Bridging the Gap Mansi M. Kasliwal, 2012 For centuries, we have known that our dynamic universe is adorned by cosmic fireworks: energetic and ephemeral beacons of light from a single star that are a million (nova) to a billion (supernova) times brighter than our sun. However, it had been an age-old conundrum that the brightest nova is approximately 1000 times fainter than the faintest supernova; why should nature leave such a wide gap? In search of an answer, I undertook three systematic surveys for my thesis. Since I was looking for transients fainter, faster and rarer than supernovae, I focused my search on galaxies in the local universe. We now have convincing evidence of multiple, distinct populations of rare transients bridging this gap. Perhaps, we are witnessing new stellar physics- shell detonations in ultra-compact white dwarf binaries, electron-capture supernovae, white dwarfs collapsing into neutron stars and birth of black-holes. A small number of intensively followed-up discoveries of elusive transients sets the stage for population studies with the upcoming Large Synoptic Survey Telescope. This effort works towards building a complete inventory of transients in the local universe (d |
| caltech data science masters: Engineering & Science , 1961 |
| caltech data science masters: Coding the Matrix Philip N. Klein, 2013-07 An engaging introduction to vectors and matrices and the algorithms that operate on them, intended for the student who knows how to program. Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by doing, writing programs to implement the mathematical concepts and using them to carry out tasks and explore the applications. Examples include: error-correcting codes, transformations in graphics, face detection, encryption and secret-sharing, integer factoring, removing perspective from an image, PageRank (Google's ranking algorithm), and cancer detection from cell features. A companion web site, codingthematrix.com provides data and support code. Most of the assignments can be auto-graded online. Over two hundred illustrations, including a selection of relevant xkcd comics. Chapters: The Function, The Field, The Vector, The Vector Space, The Matrix, The Basis, Dimension, Gaussian Elimination, The Inner Product, Special Bases, The Singular Value Decomposition, The Eigenvector, The Linear Program A new edition of this text, incorporating corrections and an expanded index, has been issued as of September 4, 2013, and will soon be available on Amazon. |
| caltech data science masters: Practical Predictive Analytics and Decisioning Systems for Medicine Gary D. Miner, Linda A. Miner, Mitchell Goldstein, Robert Nisbet, Nephi Walton, Pat Bolding, Joseph Hilbe, Thomas Hill, 2014-09-27 With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner.Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. - Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research - Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations - Demonstrates methods to help sort through data to make better observations and allow you to make better predictions |
| caltech data science masters: Number Theory for Beginners Andre Weil, 2012-12-06 In the summer quarter of 1949, I taught a ten-weeks introductory course on number theory at the University of Chicago; it was announced in the catalogue as Alge bra 251. What made it possible, in the form which I had planned for it, was the fact that Max Rosenlicht, now of the University of California at Berkeley, was then my assistant. According to his recollection, this was the first and last time, in the his tory of the Chicago department of mathematics, that an assistant worked for his salary. The course consisted of two lectures a week, supplemented by a weekly laboratory period where students were given exercises which they were. asked to solve under Max's supervision and (when necessary) with his help. This idea was borrowed from the Praktikum of German universi ties. Being alien to the local tradition, it did not work out as well as I had hoped, and student attendance at the problem sessions so on became desultory. v vi Weekly notes were written up by Max Rosenlicht and issued week by week to the students. Rather than a literal reproduction of the course, they should be regarded as its skeleton; they were supplemented by references to stan dard text-books on algebra. Max also contributed by far the larger part of the exercises. None of ,this was meant for publication. |
| caltech data science masters: Women in the Geosciences Mary Anne Holmes, Suzanne OConnell, Kuheli Dutt, 2015-05-26 Read an interview with the author: Working Toward Gender Parity in the Geosciences The geoscience workforce has a lower proportion of women compared to the general population of the United States and compared to many other STEM fields. This volume explores issues pertaining to gender parity in the geosciences, and sheds light on some of the best practices that increase participation by women and promote parity. Volume highlights include: • Lessons learned from NSF-ADVANCE • Data on gender composition of faculty at top earth science institutions in the US • Implicit bias and gender as a social structure • Strategies for institutional change • Dual career couples • Family friendly policies • Role of mentoring • Career advancement for women • Recruiting diverse faculty • Models of institutional transformation Women in the Geosciences is a valuable contribution to the existing literature on gender issues in STEM disciplines. It focuses specifically on the geosciences, with a goal to spreading awareness on the best practices for gender parity in academic geoscience departments. Geoscientists, policymakers, educators and administrators could all greatly benefit from the contents of this volume. |
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