Engineering Of Computer Applications
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This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing.
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Stanford University offers a Professional Certificate in IT Benchmarking that can be earned entirely through part-time online study. The program is administered through the Stanford Center for Professional Development. Requirements and Costs: The certificate requires successful completion of 4 courses, each of which take approximately 1 hour to complete. Each course costs $125, so the full certificate can be completed for $600. More...more
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Stanford University offers a Master of Science in Mechanical Engineering that can be earned entirely through part-time online study. The program is administered through the Stanford Center for Professional Development. Admissions: Applicants to the online program must meet the same standards as applicants to the traditional on-campus program. More information is available through the Stanford website. Students may begin taking...more
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Stanford University offers a Master of Science in Electrical Engineering that can be earned entirely through part-time online study. The program is administered through the Stanford Center for Professional Development. Admissions: Applicants to the online program must meet the same standards as applicants to the traditional on-campus program. More information is available through the Stanford website. Students may begin taking...more
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Topics include: Advanced memory management features of C and C++; the differences between imperative and object-oriented paradigms; the functional paradigm (using LISP) and concurrent programming (using C and C++); brief survey of other modern languages such as Python, Objective C, and C#. Prerequisites: Programming and problem solving at the Programming Abstractions level. Prospective students should know a reasonable amount of C++....more
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Introduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems. Topics include: Least-squares aproximations of over-determined equations and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm and singular value decomposition. Eigenvalues, left and right eigenvectors, and dynamical interpretation. Matrix...more
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This course covers the derivation of symmetry theory; lattices, point groups, space groups, and their properties; use of symmetry in tensor representation of crystal properties, including anisotropy and representation surfaces; and applications to piezoelectricity and elasticity.
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The internal organization and operation of digital computers. Machine architecture, support for high-level languages (logic, arithmetic, instruction sequencing) and operating systems (I/O, interrupts, memory management, process switching). Elements of computer logic design. Tradeoffs involved in fundamental architectural design decisions.
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This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python™ programming language.
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Continuation of Convex Optimization I. Topics include: Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation. Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and...more
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Fundamental dynamic data structures, including linear lists, queues, trees, and other linked structures; arrays strings, and hash tables. Storage management. Elementary principles of software engineering. Abstract data types. Algorithms for sorting and searching. Introduction to the Java programming language.
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This graduate-level course is a continuation of Computational Science and Engineering I. Topics include numerical methods; initial-value problems; network flows; and optimization.
