Introduction to Algorithms

Author: Thomas H. Cormen

Publisher: MIT Press

ISBN: 0262533057

Category: Computers

Page: 1292

View: 7006

A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow.

Algorithms Unlocked

Author: Thomas H. Cormen

Publisher: MIT Press

ISBN: 0262313235

Category: Computers

Page: 240

View: 4906

Have you ever wondered how your GPS can find the fastest way to your destination, selecting one route from seemingly countless possibilities in mere seconds? How your credit card account number is protected when you make a purchase over the Internet? The answer is algorithms. And how do these mathematical formulations translate themselves into your GPS, your laptop, or your smart phone? This book offers an engagingly written guide to the basics of computer algorithms. In Algorithms Unlocked, Thomas Cormen -- coauthor of the leading college textbook on the subject -- provides a general explanation, with limited mathematics, of how algorithms enable computers to solve problems. Readers will learn what computer algorithms are, how to describe them, and how to evaluate them. They will discover simple ways to search for information in a computer; methods for rearranging information in a computer into a prescribed order ("sorting"); how to solve basic problems that can be modeled in a computer with a mathematical structure called a "graph" (useful for modeling road networks, dependencies among tasks, and financial relationships); how to solve problems that ask questions about strings of characters such as DNA structures; the basic principles behind cryptography; fundamentals of data compression; and even that there are some problems that no one has figured out how to solve on a computer in a reasonable amount of time.

Introduction to Machine Learning

Author: Ethem Alpaydin

Publisher: MIT Press

ISBN: 0262028182

Category: Computers

Page: 640

View: 7899

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

How to Think About Algorithms

Author: Jeff Edmonds

Publisher: Cambridge University Press

ISBN: 1139471759

Category: Computers

Page: N.A

View: 5880

This textbook, for second- or third-year students of computer science, presents insights, notations, and analogies to help them describe and think about algorithms like an expert, without grinding through lots of formal proof. Solutions to many problems are provided to let students check their progress, while class-tested PowerPoint slides are on the web for anyone running the course. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author guides students around the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. The book fosters a deeper understanding of how and why each algorithm works. These insights are presented in a careful and clear way, helping students to think abstractly and preparing them for creating their own innovative ways to solve problems.

An Introduction to Bioinformatics Algorithms

Author: Neil C. Jones,Pavel A. Pevzner,Pavel Pevzner

Publisher: MIT Press

ISBN: 9780262101066

Category: Computers

Page: 435

View: 3405

Algorithms and Complexity. Molecular Biology Primer. Exhaustive Search. Greedy Algorithms. Dynamic Programming Algorithms. Divide-and-Conquer Algorithms. Graph Algorithms. Combinatorial Pattern Matching. Clustering and Trees. Hidden Markov Models. Randomized Algorithms.

Introduction to Computation and Programming Using Python

With Application to Understanding Data

Author: John V. Guttag

Publisher: MIT Press

ISBN: 0262529629

Category: Computers

Page: 472

View: 4046

The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization.

Introduction to the Theory of Computation

Author: Michael Sipser

Publisher: Cengage Learning

ISBN: 1285401069

Category: Computers

Page: 504

View: 1124

Now you can clearly present even the most complex computational theory topics to your students with Sipser's distinct, market-leading INTRODUCTION TO THE THEORY OF COMPUTATION, 3E. The number one choice for today's computational theory course, this highly anticipated revision retains the unmatched clarity and thorough coverage that make it a leading text for upper-level undergraduate and introductory graduate students. This edition continues author Michael Sipser's well-known, approachable style with timely revisions, additional exercises, and more memorable examples in key areas. A new first-of-its-kind theoretical treatment of deterministic context-free languages is ideal for a better understanding of parsing and LR(k) grammars. This edition's refined presentation ensures a trusted accuracy and clarity that make the challenging study of computational theory accessible and intuitive to students while maintaining the subject's rigor and formalism. Readers gain a solid understanding of the fundamental mathematical properties of computer hardware, software, and applications with a blend of practical and philosophical coverage and mathematical treatments, including advanced theorems and proofs. INTRODUCTION TO THE THEORY OF COMPUTATION, 3E's comprehensive coverage makes this an ideal ongoing reference tool for those studying theoretical computing. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Introduction to Algorithms

A Creative Approach

Author: Udi Manber

Publisher: Addison Wesley

ISBN: 9780201120370

Category: Computers

Page: 478

View: 2014

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

An Introduction to the Analysis of Algorithms

Author: Robert Sedgewick,Philippe Flajolet

Publisher: Addison-Wesley

ISBN: 0133373487

Category: Computers

Page: 604

View: 7611

Despite growing interest, basic information on methods and models for mathematically analyzing algorithms has rarely been directly accessible to practitioners, researchers, or students. An Introduction to the Analysis of Algorithms, Second Edition, organizes and presents that knowledge, fully introducing primary techniques and results in the field. Robert Sedgewick and the late Philippe Flajolet have drawn from both classical mathematics and computer science, integrating discrete mathematics, elementary real analysis, combinatorics, algorithms, and data structures. They emphasize the mathematics needed to support scientific studies that can serve as the basis for predicting algorithm performance and for comparing different algorithms on the basis of performance. Techniques covered in the first half of the book include recurrences, generating functions, asymptotics, and analytic combinatorics. Structures studied in the second half of the book include permutations, trees, strings, tries, and mappings. Numerous examples are included throughout to illustrate applications to the analysis of algorithms that are playing a critical role in the evolution of our modern computational infrastructure. Improvements and additions in this new edition include Upgraded figures and code An all-new chapter introducing analytic combinatorics Simplified derivations via analytic combinatorics throughout The book’s thorough, self-contained coverage will help readers appreciate the field’s challenges, prepare them for advanced results—covered in their monograph Analytic Combinatorics and in Donald Knuth’s The Art of Computer Programming books—and provide the background they need to keep abreast of new research. "[Sedgewick and Flajolet] are not only worldwide leaders of the field, they also are masters of exposition. I am sure that every serious computer scientist will find this book rewarding in many ways." —From the Foreword by Donald E. Knuth

Reinforcement Learning

An Introduction

Author: Richard S. Sutton,Andrew G. Barto

Publisher: A Bradford Book

ISBN: 0262039249

Category: Computers

Page: 552

View: 394

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Introduction to the Design and Analysis of Algorithms

Author: Anany Levitin

Publisher: Pearson Education

ISBN: 9780273764113

Category: Juvenile Nonfiction

Page: 589

View: 9256

Based on a new classification of algorithm design techniques and a clear delineation of analysis methods, 'Introduction to the Design and Analysis of Algorithms' presents the subject in a coherent and innovative manner.

Algorithm Design: Pearson New International Edition

Author: Jon Kleinberg,Eva Tardos

Publisher: Pearson Higher Ed

ISBN: 1292037040

Category: Computers

Page: 832

View: 1070

August 6, 2009 Author, Jon Kleinberg, was recently cited in the New York Times for his statistical analysis research in the Internet age. Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science.

Introduction to Information Retrieval

Author: Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze

Publisher: Cambridge University Press

ISBN: 1139472100

Category: Computers

Page: N.A

View: 9679

Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.

An Introduction to Data Structures and Algorithms with Java

Author: Glenn W. Rowe

Publisher: Prentice Hall

ISBN: N.A

Category: Computers

Page: 445

View: 2897

Assuming only fundamental programming skills in Java, this book begins by introducing the concept of object-oriented programming in Java. Windowing Toolkit (the AWT), is also introduced at an early stage, and it is used to develop object oriented programs with graphical user interfaces (GIUs). After introducing the standard data structures and algorithms commonly studied in second year computing courses, the book concludes with a substantial case study that provides a hands-on experience with key concepts.

Algorithm Engineering and Experiments

4th International Workshop, ALENEX 2002, San Francicsco, CA, USA, January 4-5, 2002, Revised Papers

Author: David M. Mount,Clifford Stein

Publisher: Springer

ISBN: 3540456430

Category: Computers

Page: 212

View: 7170

Algorithms For Dummies

Author: John Paul Mueller

Publisher: John Wiley & Sons

ISBN: 1119330491

Category: Computers

Page: 320

View: 8919

Algorithms For Dummies addresses people who are interested in algorithms without requiring them to pursue a PhD on the subject. The idea is that we already live in a world where algorithms are behind most of the technology we use, so we need to understand them better. In order to make this happen, the book would present the major areas comprising algorithms (optimization, sort, graph, hash, string, dynamic programming) telling the history behind algorithms, presenting actual applications, simply explaining the nuts and bolts of the algorithm. For readers who are interested, the book presents an implementation in Python and some experiments with them, so they can get hands on experience developing an algorithm from start to finish. The goal is to create an accessible introduction to algorithms, so that the reader can understand how the key algorithms work and how to benefit from algorithms when working on projects and implementing them in business strategy.

Probabilistic Robotics

Author: Sebastian Thrun,Wolfram Burgard,Dieter Fox

Publisher: MIT Press

ISBN: 0262201623

Category: Technology & Engineering

Page: 647

View: 3876

Probablistic robotics is a growing area in the subject, concerned with perception and control in the face of uncertainty and giving robots a level of robustness in real-world situations. This book introduces techniques and algorithms in the field.

Machine Learning

The New AI

Author: Ethem Alpaydin

Publisher: MIT Press

ISBN: 0262529513

Category: Computers

Page: 224

View: 1290

A concise overview of machine learning -- computer programs that learn from data -- which underlies applications that include recommendation systems, face recognition, and driverless cars.

Problems on Algorithms

Author: Ian Parberry

Publisher: N.A

ISBN: 9780134335582

Category: Computers

Page: 179

View: 9882

With approximately 600 problems and 35 worked examples, this supplement provides a collection of practical problems on the design, analysis and verification of algorithms. The book focuses on the important areas of algorithm design and analysis: background material; algorithm design techniques; advanced data structures and NP-completeness; and miscellaneous problems. Algorithms are expressed in Pascal-like pseudocode supported by figures, diagrams, hints, solutions, and comments.

Algorithm Design

Author: Jon Kleinberg,Éva Tardos

Publisher: Pearson Higher Ed

ISBN: 0133072525

Category: Computers

Page: 864

View: 8270

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science. August 6, 2009 Author, Jon Kleinberg, was recently cited in the New York Times for his statistical analysis research in the Internet age.