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Master in Electrical and Computer Engineering Degree Master of Engineering Program Length 1 Year Issued By Cornell University

Develop the Technology of Tomorrow. Cornell Tech's Electrical & Computer Engineering Masters combines Cornell University's academic rigor with the energy and opportunity that come with studying in the heart of New York's tech start-up community.

The Master of Engineering in Electrical & Computer Engineering at Cornell Tech is a year-long immersive program designed to teach engineers, scientists and quantitative analysts important state-of-the art methods in signal processing, data science and decision theory. The courses in this program will teach you to think like an entrepreneur, work on cross-disciplinary teams and create a portfolio of work relevant to today's digital technology.

Exciting developments in machine learning, sensor technology, and signal and image processing are creating new opportunities for engineers and computer scientists with the advanced technical skills and entrepreneurial mindset to turn those developments into new products and services. Cornell Tech's Master in Electrical & Computer Engineering will help you gain the knowledge, hands-on experience and professional network you need to excel at the intersection of entrepreneurship, creativity and digital technology.

Learn Cutting-Edge Skills in Startup Culture

The most successful new technologies and startups develop when technologists, designers, and entrepreneurs collaborate. Cornell Tech has taken that collaborative model of product development and applied it to our degree programs. Your classroom-based courses, taught by expert faculty with deep industry experience, complement project-based experiences that will have you working in teams with students from Cornell Tech's other technical degree programs as well as our LLM and MBA programs. You'll develop your own business in Startup Studio and create innovative solutions for real business clients as part of Product Studio. In the process, you'll develop the business savvy, communication skills and exceptional technical prowess you need to attract attention from the world's most innovative companies and ambitious startups.

Who Should Apply?

You should apply to Cornell Tech's Master in Electrical & Computer Engineering if you have a passion for technology--especially robotics and machine learning--and an entrepreneurial spirit. You should also have an academic background in electrical and computer engineering, mechanical engineering, computer science, physics, applied mathematics or a related technical field. That background should include coursework in signals and systems, linear algebra, scientific programming, and probability and statistics.

Unlike the Ithaca-based ECE Masters program, Cornell Tech does not offer a third semester option for their ECE Masters students.

Topics Covered

  • Signal Processing
  • Machine Learning
  • Feedback Systems
  • Physical Computing
  • Sensing
  • Data Science
  • Decision Theory
  • Machine Learning and Computational Tools

Feedback Systems and Reinforcement Learning

Credits 3.00

This core ECE course covers digital control systems, Markov decision processes and reinforcement learning algorithms. The first half of the course covers the fundamentals of modern digital control systems, including state space models and their analysis, state variable feedback and the basics of system identification. The second half of the course deals with Markov decision processes with applications in social sensing and communication systems. The topics covered include stochastic dynamic programming, simulation based optimization and reinforcement learning algorithms. Throughout the course, applications in discrete event systems and machine learning will be emphasized.


Signal and Data Processing

Credits 3.00

This core ECE course covers the basics of signal processing and data analysis. The first half of the course covers the fundamentals of signals and systems, including the discrete Fourier transform, transfer functions, adaptive filtering and applications in noise cancellation and communication systems. The second half covers the basics of probabilistic models, stochastic simulation Markov processes and Bayesian inference. Finally, topics in high dimensional signal processing including model selection, sparse signal processing and principal component analysis are covered. Throughout the course, applications in communication systems, sensing systems and machine learning will be emphasized.

Careers in the Field

  • Data Scientist
  • Financial Modeler
  • Research Engineer
  • Social Network Analyst
  • Startup Founder or CTO

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