Validating solutions computer 65 major problems management
These concepts are used to illustrate wider concepts in the design of other large software systems, including simplicity; efficiency; event-driven programming; abstraction design; client-server architecture; mechanism vs.policy; orthogonality; naming and binding; static vs. time, and other tradeoffs; optimization; caching; and managing large codebases. Performance, reliability, privacy, replication, and backup. A major portion of the course is devoted to readings selected from current research in the field. Design techniques including divide-and-conquer and dynamic programming.Applications including sorting and searching, graph theoretic problems such as shortest path and network flow, and topics selected from arithmetic circuits, parallel algorithms, computational geometry, and others.An introduction to computational complexity, NP-completeness, and approximation algorithms. Typical projects include measurement of databases, theorem provers, file systems, networks, OS kernels, and computer processors.This course explores how to design a new programming language.
Brief review of computability theory through Rice’s Theorem and the Recursion Theorem followed by a rigorous treatment of complexity theory. Modeling of physical problems, computer implementation, analysis of results; use of mathematical software; numerical methods chosen from: solutions of linear and nonlinear algebraic equations, solutions of ordinary and partial differential equations, finite elements, linear programming, optimization algorithms, and fast-Fourier transforms. In a nutshell, it examines the question: What does (will) it take for computers to perform human tasks?
This course fulfills the computer science core requirement at Harvey Mudd College. Successful completion of this course satisfies the Computer Science 5 core requirement and Computer Science 60 coursework. Information structures, functional programming, object-oriented programming, grammars, logic, logic programming, correctness, algorithms, complexity analysis, finite-state machines, basic processor architecture, and theoretical limitations.
It does not fulfill the HMC biology core requirement. Those who have completed Computer Science 42 cannot take Computer Science 60.
The complexity classes P, NP, and the Cook-Levin Theorem. The polynomial hierarchy, PSPACE-completeness, L and NL-completeness, #P-completeness. The objective of this course is to explore sophisticated algorithm design and analysis techniques that are generally not taught in a first algorithms course. Effective graphical and tabular presentation of data. It presents a broad introduction to topics such as knowledge representation, search, learning, and reasoning under uncertainty.
The course addresses topics such as graph matching, competitive analysis of online algorithms, matroid theory, and approximation algorithms and schemes. For each topic, it examines real-world applications of core techniques to problems which may include game playing, text classification, and visual pattern recognition.Group projects provide experience in working with and extending a real operating system. complex instruction set architecture, pipelining, instruction-level parallelism, superscalar architectures, advanced memory-hierarchy design, advanced computer arithmetic, multiprocessor systems, cache coherence, interconnection networks, performance analysis and case studies. Characteristics of nonvolatile storage, including magnetic disks and solid-state memories. Analysis techniques including solutions to recurrence relations and amortization.