Electrical Engineering and Computer Science MIT Open. Course. Ware. A readout module form the HERA B silicon vertex detector. Image courtesy of Dr Max on Flickr. Featured Courses. Graduates of MITs electrical engineering and computer science department work in diverse industries and conduct research in a broad range of areas. They improve the stability and security of computers and communications networks, and they increase the efficiency of solar panels. They create unique algorithms to analyze financial markets and design robots capable of thinking like human beings. Our community members continually make breakthroughs that enable people to communicate more easily, manage their environments more effectively, and lead more comfortable lives than ever before. MIT has awarded electrical engineering degrees for nearly 1. We provide an in depth education in engineering principles built on mathematics, computation, and the physical and life sciences, and encourage our students to apply what they learn through projects, internships, and research. We succeed in our mission to produce graduates capable of taking leadership positions in the fields of electrical engineering and computer science and beyond. More than 3. 0 percent of MITs undergraduates are enrolled in the Department of Electrical Engineering and Computer Science, and our graduate programs are world renowned. Our faculty comprises more than 4. National Academy of Engineering, more than 1. National Academy of Sciences, several National Medal of Technology winners, as well as many fellows of professional societies, such as the IEEE, ACM, APS, AAAI and others. Electrical Engineering and Computer Science. Courses. No courses match the topics and filters you have selected. Ocean Wave Interaction with Ships and Offshore Energy Systems 1. Some Description. InstructorsProf. As Taught In. Spring 2. Course Number. Level. UndergraduateGraduate. Features. Lecture Notes, Student Work. Latest MATLAB projects for Engineering students 2015, also image processing projects and signal processing project ideas also with sources. Lukas and I were trying to write a succinct comparison of the most popular packages that are typically used for data analysis. I think most people choose one based on. Gary Scotts collection of source code links. TlXGV2TR8I/S97ZuNIrm9I/AAAAAAAAAOY/UXbLIWJTIzo/s1600/Scilab.png' alt='Computer Oriented Numerical Methods Matlab Programs' title='Computer Oriented Numerical Methods Matlab Programs' />Archived Electrical Engineering and Computer Science Courses. Some prior versions of courses listed above have been archived in OCWs DSpaceMIT repository for long term access and preservation. Links to archived prior versions of a course may be found on that courses Other Versions tab. Additionally, the Archived Electrical Engineering and Computer Science Courses page has links to every archived course from this department. Management Science and Engineering Stanford University. Courses. MS E 2. Discrete Probability Concepts And Models. Units. Fundamental concepts and tools for the analysis of problems under uncertainty, focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, belief networks, random variables, conditioning, and expectation. The course is fast paced, but it has no prerequisites. Driver Pci Device Placa Gigabyte. MS E 5. 2. Introduction to Decision Making. Units. How to ensure focus, discipline, and passion when making important decisions. Comprehensive examples illustrate Decision Analysis fundamentals. Consulting case studies highlight practical solutions for real decisions. Student teams present insights from their analyses of decisions for current organizations. Topics declaring when and how to make a decision, framing and structuring the decision basis, defining values and preferences, creating alternative strategies, assessing unbiased probabilistic judgments, developing appropriate riskreward and portfolio models, evaluating doable strategies across the range of uncertain future scenarios, analyzing relevant sensitivities, determining the value of additional information, and addressing the qualitative aspects of communication and commitment to implementation. Not intended for MS E majors. MS E 9. 2Q. International Environmental Policy. Units. Preference to sophomores. Science, economics, and politics of international environmental policy. Current negotiations on global climate change, including actors and potential solutions. Sources include briefing materials used in international negotiations and the U. S. Congress. MS E 9. Q. Nuclear Weapons, Energy, Proliferation, and Terrorism. Units. Preference to sophomores. At least 2. 0 countries have built or considered building nuclear weapons. However, the paths these countries took in realizing their nuclear ambitions vary immensely. Why is this the caseHow do the histories, cultures, national identities, and leadership of these countries affect the trajectory and success of their nuclear programs This seminar will address these and other questions about nuclear weapons and their proliferation. Computer Oriented Numerical Methods Matlab Programs' title='Computer Oriented Numerical Methods Matlab Programs' />Students will learn the fundamentals of nuclear technology, including nuclear weapons and nuclear energy, and be expected to use this knowledge in individual research projects on the nuclear weapons programs of individual countries. Case studies will include France, UK, China, India, Israel, Pakistan, North Korea, South Africa, Libya, Iraq, and Iran, among others. Please note any language skills in your application. Recommended 1. 93 or 2. MS E 1. 08. Senior Project. Units. Restricted to MS E majors in their senior year. Students carry out a major project in groups of four, applying techniques and concepts learned in the major. Numerical Methods in Engineering with Python Numerical Methods in Engineering with Python is a text for engineering students and a reference for practicing engineers. Compendium of all course descriptions for courses available at Reynolds Community College. A Guide to MATLAB. This book is a short, focused introduction to MATLAB, a comprehensive software system for mathematics and technical computing. Computer Oriented Numerical Methods Matlab Programs' title='Computer Oriented Numerical Methods Matlab Programs' />Project work includes problem identification and definition, data collection and synthesis, modeling, development of feasible solutions, and presentation of results. Service Learning Course certified by Haas Center. Satisfies the WIM requirement for MS E majors. MS E 1. 11. Introduction to Optimization. Units. Formulation and computational analysis of linear, quadratic, and other convex optimization problems. Applications in machine learning, operations, marketing, finance, and economics. Prerequisite CME 1. MATH 5. 1. Same as ENGR 6. MS E 2. 11. MS E 1. A-Primer-on-Scientific-Programming-with-Python-5th-ed-Hans-Petter-Langtangen-922pd.jpg' alt='Computer Oriented Numerical Methods Matlab Programs' title='Computer Oriented Numerical Methods Matlab Programs' />X. Introduction to Optimization Accelerated. Units. Optimization theory and modeling. The role of prices, duality, optimality conditions, and algorithms in finding and recognizing solutions. Perspectives problem formulation, analytical theory, computational methods, and recent applications in engineering, finance, and economics. Theories finite dimensional derivatives, convexity, optimality, duality, and sensitivity. Methods simplex and interior point, gradient, Newton, and barrier. Prerequisite CME 1. MATH 5. 1 or equivalent. Same as ENGR 6. 2X, MS E 2. XMS E 1. 12. Mathematical Programming and Combinatorial Optimization. Units. Combinatorial and mathematical programming integer and non linear techniques for optimization. Topics linear program duality and LP solvers integer programming combinatorial optimization problems on networks including minimum spanning trees, shortest paths, and network flows matching and assignment problems dynamic programming linear approximations to convex programs NP completeness. Hands on exercises. Prerequisites 1. MATH 1. CS 1. A or X. Same as MS E 2. MS E 1. 20. Probabilistic Analysis. Units. Concepts and tools for the analysis of problems under uncertainty, focusing on focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems. Prerequisite CME 1. MATH 5. 1. MS E 1. Introduction to Stochastic Modeling. Units. Stochastic processes and models in operations research. Discrete and continuous time parameter Markov chains. Queuing theory, inventory theory, simulation. Prerequisite 1. 20, 1. MS E 1. 25. Introduction to Applied Statistics. Units. An increasing amount of data is now generated in a variety of disciplines, ranging from finance and economics, to the natural and social sciences. Making use of this information, however, requires both statistical tools and an understanding of how the substantive scientific questions should drive the analysis. In this hands on course, we learn to explore and analyze real world datasets. We cover techniques for summarizing and describing data, methods for statistical inference, and principles for effectively communicating results. Prerequisite 1. 20, CS 1. A, or equivalents. MS E 1. 30. Information Networks and Services. Units. Architecture of the Internet and performance engineering of computer systems and networks. Switching, routing and shortest path algorithms. Congestion management and queueing networks. Peer to peer networking. Wireless and mobile networking. Information service engineering and management. Search engines and recommendation systems. Reputation systems and social networking technologies. Security and trust. Information markets. Select special topics and case studies. Prerequisites 1. CS 1. A. MS E 1. Networks. Units. This course provides an introduction to how networks underly our social, technological, and natural worlds, with an emphasis on developing intuitions for broadly applicable concepts in network analysis. The course will include an introduction to graph theory and graph concepts social networks information networks the aggregate behavior of markets and crowds network dynamics information diffusion the implications of popular concepts such as six degrees of separation, the friendship paradox, and the wisdom of crowds. MS E 1. 40. Accounting for Managers and Entrepreneurs. Units. Non majors and minors who have taken or are taking elementary accounting should not enroll. Introduction to accounting concepts and the operating characteristics of accounting systems. The principles of financial and cost accounting, design of accounting systems, techniques of analysis, and cost control. Interpretation and use of accounting information for decision making.