Integral Methods in Science and Engineering: Theoretical and Practical Aspects

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The volume may be used as a reference guide and a practical resource.

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It is suitable for researchers and practitioners in applied mathematics, physics, and mechanical and electrical engineering, as well as graduate students in these disciplines. Read more Read less. Amazon Global Store US International products have separate terms, are sold from abroad and may differ from local products, including fit, age ratings, and language of product, labeling or instructions. Manufacturer warranty may not apply Learn more about Amazon Global Store. From the Back Cover The quantitative and qualitative study of the physical world makes use of many mathematical models governed by a great diversity of ordinary, partial differential, integral, and integro-differential equations.

No customer reviews. Share your thoughts with other customers. Write a customer review. Discover the best of shopping and entertainment with Amazon Prime. Prime members enjoy FREE Delivery on millions of eligible domestic and international items, in addition to exclusive access to movies, TV shows, and more. Introduction to Computer Science in Industry. This course will introduce students to CS in industry through weekly overview talks by alums and engineers in industry. It is aimed at second-year undergraduates. Others may wish to take the course to gain an understanding of the scope of computer science in industry.

Additionally students will complete short weekly assignments aimed at preparing them for interactions with industry. This course is closed to first and second term freshman for credit. Decidability and Tractability. Prerequisite: CS 2 may be taken concurrently. This course introduces the formal foundations of computer science, the fundamental limits of computation, and the limits of efficient computation. Topics will include automata and Turing machines, decidability and undecidability, reductions between computational problems, and the theory of NP-completeness.

Introduction to Computing Systems. Basic introduction to computer systems, including hardware-software interface, computer architecture, and operating systems. Course emphasizes computer system abstractions and the hardware and software techniques necessary to support them, including virtualization e. Algorithms in the Real World.

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This course introduces algorithms in the context of their usage in the real world. The course covers compression, advanced data structures, numerical algorithms, cryptography, computer algebra, and parallelism.

Pdf Integral Methods In Science And Engineering Theoretical And Practical Aspects 2006

The goal of the course is for students to see how to use theoretical algorithms in real-world contexts, focusing both on correctness and the nitty-gritty details and optimizations. Implementations focus on two orthogonal avenues: speed for which C is used and algorithmic thinking for which Python is used. This course introduces techniques for the design and analysis of efficient algorithms.

Major design techniques the greedy approach, divide and conquer, dynamic programming, linear programming will be introduced through a variety of algebraic, graph, and optimization problems. Methods for identifying intractability via NP-completeness will be discussed.

Computer Science Education in K Settings. This course will focus on computer science education in K settings.

The Math Needed for Computer Science

Students will gain an understanding of the current state of computer science education within the United States, develop curricula targeted at students from diverse backgrounds, and gain hands on teaching experience. Through readings from educational psychology and neuropsychology, students will become familiar with various pedagogical methods and theories of learning, while applying these in practice as part of a teaching group partnered with a local school or community college. Each week students are expected to spend about 2 hours teaching, 2 hours developing curricula, and 2 hours on readings and individual exercises.

May not be repeated. Principles of Microprocessor Systems. The principles and design of microprocessor-based computer systems. Lectures cover both hardware and software aspects of microprocessor system design such as interfacing to input and output devices, user interface design, real-time systems, and table-driven software. The homework emphasis is on software development, especially interfacing with hardware, in assembly language. Microprocessor Systems Laboratory.

The student will design, build, and program a specified microprocessor-based system. This structured laboratory is organized to familiarize the student with electronic circuit construction techniques, modern development facilities, and standard design techniques. The lectures cover topics in microprocessor system design such as display technologies, interfacing with analog systems, and programming microprocessors in high-level languages.

Microprocessor Project Laboratory. A project laboratory to permit the student to select, design, and build a microprocessor-based system. The student is expected to take a project from proposal through design and implementation possibly including PCB fabrication to final review and documentation. Multidisciplinary Systems Engineering. This course presents the fundamentals of modern multidisciplinary systems engineering in the context of a substantial design project.

Students from a variety of disciplines will conceive, design, implement, and operate a system involving electrical, information, and mechanical engineering components. Specific tools will be provided for setting project goals and objectives, managing interfaces between component subsystems, working in design teams, and tracking progress against tasks. Students will be expected to apply knowledge from other courses at Caltech in designing and implementing specific subsystems. During the first two terms of the course, students will attend project meetings and learn some basic tools for project design, while taking courses in CS, EE, and ME that are related to the course project.

During the third term, the entire team will build, document, and demonstrate the course design project, which will differ from year to year. Freshmen must receive permission from the lead instructor to enroll.

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CS 80 abc. Individual research project, carried out under the supervision of a member of the computer science faculty or other faculty as approved by the computer science undergraduate option representative. Open only to upperclass students. CS 81 a. Undergraduate Projects in Computer Science. Units are assigned in accordance with work accomplished.

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Supervised research or development in computer science by undergraduates. This course can with approval be used to satisfy the project requirement for the CS major. Undergraduate Reading in Computer Science. Supervised reading in computer science by undergraduates. The topic must be approved by the reading supervisor, and a formal final report must be presented on completion of the term. CS abc. Special Topics in Computer Science. Units in accordance with work accomplished; offered by announcement. The topics covered vary from year to year, depending on the students and staff.

Primarily for undergraduates.

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Seminar in Computer Science. Reading in Computer Science. Causation and Explanation.

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An examination of theories of causation and explanation in philosophy and neighboring disciplines. Topics discussed may include probabilistic and counterfactual treatments of causation, the role of statistical evidence and experimentation in causal inference, and the deductive-nomological model of explanation. The treatment of these topics by important figures from the history of philosophy such as Aristotle, Descartes, and Hume may also be considered.

Programming Practicum. A self-paced lab that provides students with extra practice and supervision in transferring their programming skills to a particular programming language. This course is available for graduate students only. Undergraduates should register for CS Functional Programming. Prerequisites: CS 1 and CS 4.

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  • This course is a both a theoretical and practical introduction to functional programming, a paradigm which allows programmers to work at an extremely high level of abstraction while simultaneously avoiding large classes of bugs that plague more conventional imperative and object-oriented languages. The course will introduce and use the lazy functional language Haskell exclusively.