Clojure Programming

There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.

Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. Accompanying the mathematical derivations are full implementations of dozens of numerical methods in Python, as well as more than 250 end-of-chapter problems. Numerical methods and physics examples are clearly separated, allowing this introductory book to be later used as a reference; the penultimate section in each chapter is an in depth project, tackling physics problems which cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the

Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. Accompanying the mathematical derivations are full implementations of dozens of numerical methods in Python, as well as more than 250 end-of-chapter problems. Numerical methods and physics examples are clearly separated, allowing this introductory book to be later used as a reference; the penultimate section in each chapter is an in depth project, tackling physics problems which cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the

CBC and NSTA's Best STEM Books Pick for 2022Explore artificial intelligence—the programming that teaches computers to be brainy—in this “enjoyable, upbeat series” (Booklist).Did you know that you can teach a robot to play soccer? Or that you can ride in a car driven by a computer? Discover how AI learns and grows, as well as how it identifies objects by recognizing differences and similarities!Ignite your scientific mind with fun facts, activities, and experiments. Try this: Hum “Twinkle, Twinkle Little Star,” “Baa, Baa, Black Sheep,” and the ABCs. Do you notice any similarities? Soon you’ll be thinking just like an AI program!This STEM-driven series helps beginning scientists and engineers grasp big scientific ideas with:Intriguing info to fill and thrill your brain.Hands-on activities to jumpstart your imagination.Great photos to help you get the bigger picture.New ideas sure to change the way you see the world.From Starry Forest Books,STEAM & Me™uses science, technology, enginee

This book is devoted to five main principles of algorithm design: divide and conquer, greedy algorithms, thinning, dynamic programming, and exhaustive search. These principles are presented using Haskell, a purely functional language, leading to simpler explanations and shorter programs than would be obtained with imperative languages. Carefully selected examples, both new and standard, reveal the commonalities and highlight the differences between algorithms. The algorithm developments use equational reasoning where applicable, clarifying the applicability conditions and correctness arguments. Every chapter concludes with exercises (nearly 300 in total), each with complete answers, allowing the reader to consolidate their understanding and apply the techniques to a range of problems. The book serves students (both undergraduate and postgraduate), researchers, teachers, and professionals who want to know more about what goes into a good algorithm and how such algorithms can be expresse

Building upon the success of the first edition, Statistics Using Stata uses the latest version of Stata to meet the needs of today's students. Engaging and accessible for students from a variety of mathematical backgrounds, this textbook integrates statistical concepts with the Stata (version 16) software package. It aligns Stata commands with examples based on real data, enabling students to understand statistics in a way that reflects statistical practice. Capitalizing on Stata's menu-driven 'point and click' and program syntax interface, the chapters guide students from the comfortable 'point and click' environment to the beginnings of statistical programming. Its coverage of essential topics gives instructors flexibility in curriculum planning and provides students with more advanced material to prepare for future work. Online resources - including solutions to exercises, PowerPoint slides, and Stata syntax (do-files) for each chapter - allow students to review independently and ad

Finite-state methods are the most efficient mechanisms for analysing textual and symbolic data, providing elegant solutions for an immense number of practical problems in computational linguistics and computer science. This book for graduate students and researchers gives a complete coverage of the field, starting from a conceptual introduction and building to advanced topics and applications. The central finite-state technologies are introduced with mathematical rigour, ranging from simple finite-state automata to transducers and bimachines as 'input-output' devices. Special attention is given to the rich possibilities of simplifying, transforming and combining finite-state devices. All algorithms presented are accompanied by full correctness proofs and executable source code in a new programming language, C(M), which focuses on transparency of steps and simplicity of code. Thus, by enabling readers to obtain a deep formal understanding of the subject and to put finite-state methods t

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This unique text provides a thorough, yet accessible, grounding in the mathematics, statistics, and programming that students need to master for coursework and research in climate science, meteorology, and oceanography. Assuming only high school mathematics, it presents carefully selected concepts and techniques in linear algebra, statistics, computing, calculus and differential equations within the context of real climate science examples. Computational techniques are integrated to demonstrate how to visualize, analyze, and apply climate data, with R code featured in the book and both R and Python code available online. Exercises are provided at the end of each chapter with selected solutions available to students to aid self-study and further solutions provided online for instructors only. Additional online supplements to aid classroom teaching include datasets, images, and animations. Guidance is provided on how the book can support a variety of courses at different levels, making i

A primer on the underlying technologies that allow computer programs to work. Covers topics like computer hardware, combinatorial logic, sequential logic, computer architecture, computer anatomy, and