Chateease - Validating solutions computer 65 major problems management
Proficiency with programming is expected as some assignments require algorithm implementation. Computational techniques applied to problems in the sciences and engineering. This course presents a general introduction to the field of Artificial Intelligence.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 introduces fundamental concepts from the core course Computer Science 5 using biology as the context for those computational ideas.
This course explores how to design a new programming language.
In particular, we’ll focus on “Domain-Specific Languages”—languages designed for people who want to use a computer to perform a specialized task (e.g., to compose music or query a database or make games).
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.
Measurement and analysis of computer software and systems performance, with emphasis on methodological issues. Modeling, simulation, and analysis of artificial neural networks and their relation to biological networks. Applications chosen from function approximation, signal processing, control, computer graphics, pattern recognition, and time-series analysis.