The Master in Electrical and Computer Engineering curriculum includes a variety of rigorous courses that will give you advanced skills in machine learning, signal processing, computer modeling and other cutting-edge disciplines.

In an educational innovation unique to Cornell Tech, you'll also participate in an immersive Studio experience, in which you'll hone your team-building and leadership skills while developing a new product idea in response to the strategic needs of a real organization and create your own startup.

What Your Schedule Might Look Like

Students must complete a total of 30 credit hours over two semesters. Core courses must total a minimum of 12 credits.

Students must complete a total of 30 credit hours over two semesters. Core courses must total a minimum of 12 credits. During the Spring Semester, Physical Computing and Bayesian Estimation & Learning are required courses. You will be able to chose between five and six credits from the other Technical Courses listed below:

Technical credits 9.00

Studio credits 6.00

Semester Total 15 credits

Technical credits 11.00

Studio credits 5.00

Semester Total 16 credits

Technical Courses

Your core technical curriculum
  • Signal and Data Processing 3.0

    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.

  • Feedback Systems and Reinforcement Learning 3.0

    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.

  • Optimization Methods 3.0

    This course covers algorithmic and computational tools for solving optimization problems with the goal of providing decision-support for business intelligence. We will cover the fundamentals of linear, integer and nonlinear optimization. We will emphasize optimization as a large-scale computational tool, and show how to link programming languages, such as Python, Java and C++, with optimization software, such as Gurobi and CPLEX, to develop industrial-strength decision-support systems. We will demonstrate how to incorporate uncertainty into optimization problems. Throughout the course, we will cover a variety of modern applications and show how to deploy large-scale optimization models.

  • Physical Computing 3.0

    This course provides a hands-on introduction to the resources for designing and fabricating smart systems using hardware components including sensors and sensor networks, analog instrumentation, embedded digital processing (microcontroller programming such as the Arduino system), graphics and I/O chips, flash memory, wired and wireless communications, PCB layout and fabrication, 3-D printing, and laser cutting. The key characteristics of the components and their interfaces will be presented. Using these tools, small multidisciplinary groups will conduct a hardware project of their choice.

  • Bayesian Estimation & Learning 3.0

  • Natural Language Processing 3.0

    This course constitutes an introduction to natural language processing (NLP), the goal of which is to enable computers to use human languages as input, output, or both. NLP is at the heart of many of today's most exciting technological achievements, including machine translation, automatic conversational assistants and Internet search. Possible topics include summarization, machine translation, sentiment analysis and information extraction as well as methods for handling the underlying phenomena (e.g., syntactic analysis, word sense disambiguation, and discourse analysis).

  • Data Science in the Wild 3.0

    Companies and organizations collect massive amounts of data, and the task of a data scientist is to extract actionable knowledge from the data – for scientific needs, to improve public health, to promote businesses, for social studies and for various other purposes. This course will focus on the practical aspects of the field and will attempt to provide a comprehensive set of tools for extracting knowledge from data.

  • Applied Machine Learning 3.0

    This course will help students learn and apply key concepts of modeling, analysis and validation from Machine Learning, Data Mining and Signal Processing to analyze and extract meaning from data. Students will implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. They will also gain a working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, association rule mining and dimensionality reduction.

  • Computer Vision 3.0

    The goal of computer vision is to compute properties of the three-dimensional world from digital images. Problems in this field include reconstructing the three-dimensional shape of an environment, determining how things are moving, and recognizing people and objects and their activities, all through analysis of images and videos. This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation and mobile computer vision. This is a project-based course, in which students will implement several computer vision algorithms throughout the semester.

  • Design Thinking 1.0

    This hands-on course will prepare students to be future innovators by teaching Design Thinking, the human-centered design methodology pioneered by IDEO and Stanford d.school founder, David Kelley. Students will work on a team with peers from other disciplines so as to experience the importance of “radical collaboration.” All teams will work on the same challenge, and students will be asked to design an innovative solution to this complex problem.

  • Law for Non-Lawyers (for non-LLM students) 1.0

    This class introduces the principal legal issues involved in starting, managing and operating a technology-oriented business by entrepreneurs. It is intended to provide non-law students with an understanding of many of the laws and regulations to which developing businesses in the United States tech sector are typically subject—from the time an entrepreneur conceives and begins to build a business, implements a business plan, and obtains financing, to when she begins operations in anticipation of managing a mature company and considering possible exit strategies. The instructor, a former corporate partner in a large New York City law firm, will adopt the role of a general counsel to a start-up company advising his client/students about how laws and regulations affect their businesses at various stages of development, as well as about typical key contractual terms and negotiating strategies. Practicing lawyers will serve as guest lecturers. The course is designed to impart an understanding not only about substantive areas of the law that intersect with tech businesses but also about the role that lawyers should—and should not—play in burgeoning business enterprises. Students will gain insights into how lawyers approach business problems and the benefits and limitations of such a perspective.

Studio & Interdisciplinary Courses

Practicums with other Cornell Tech Masters students.
  • Product Studio 3.0

    In Product Studio you and a team of your classmates will respond to a "Company Challenge" by developing, testing, and presenting a new product or business idea. Previous challenges have been posed by organizations as diverse as the Robin Hood Foundation, Uber, Weight Watchers and Bloomberg.

  • Startup Ideas 1.0

    This studio-based course helps students develop their ability to imagine, recognize, develop and improve startup ideas. In each class, students learn a different approach to product ideation or product critique, then practice that approach, working in many different teams -- often with the advice of visiting entrepreneurs, VCs, domain experts, and other practitioners. Students invent and explore hundreds of startup ideas, and help each other evaluate and improve those ideas. By the end of the course, students self-organize into co-founding teams around specific startup ideas that they will pursue in Startup Studio the following semester.

  • Business for Tech 1.0

    Business for Technologists (BT) is a comprehensive introduction to the key aspects of new product development and product management that will provide helpful perspective for technologists working in cross functional teams with business leaders in the marketing, product, finance, legal and business development disciplines. BT focuses both on concepts and frameworks utilizing “solutions architecture” while working on cross functional teams in both agile and waterfall development environments. Students will utilize their team projects as the basis to complete customer development, market segmentation, product placement, construct business models and define “proof points” and milestones that lead to successful outcomes for teams comprised of both technologists and subject matter experts from a broad array of business disciplines. The course will also discuss the development of an “intellectual property suite” and its importance in the new product development process and relevance to the competitive landscape. While the course focuses on scalable businesses, the principles apply to businesses of all sizes. Topics covered will include definition of the market, sales and distribution, competition, business development, project management and milestone based performance tracking. The course culminates in a capstone project of writing a launch plan for a new technology product’s market test.

  • Leadership for Studio 1.0

    This course deals broadly with leadership – of teams, projects, products, businesses, and communities. The course will pay special attention to leadership in the context of digital transformation and its social and economic impacts. Students will learn effective team-building and teamwork strategies, communication and presentation skills, and best practices for building a collaborative, creative and open culture in the workplace. As part of a personal development process, toolkits and exercises will be provided to promote critical thinking and moral reasoning skills. Sessions on social and multicultural awareness and sensitivity will equip students to be global leaders in a digitally-transformed world. In short, this course will cover all aspects of how students can become leaders in a digital economy.

  • Conversations in the Studio 1.0

    This course features a weekly guest practitioner for a provocative, closed-door discussion with students. The guest practitioners are active entrepreneurs, intrapreneurs, social entrepreneurs, engineers, designers, artists, VCs, lawyers, writers, ethicists, and other diverse leaders who are affecting society through their entrepreneurial efforts. Conversations take place in the Cornell Tech Studio and are moderated each week by a randomly assigned group of students who come prepared with questions and discussion topics. This is not a lecture: it's a weekly wake-up call.

  • Startup Studio 3.0

    In Startup Studio you and a team of your classmates will develop your own new product or startup idea. You'll experience the entire process, from developing your idea, to prototyping and testing, to pitching to investors. You can even apply for a Startup Award that will provide funding and other support to help you turn your Startup Studio project to a real business.

  • Product Management 1.0

    This studio-based course helps students learn about and develop product management (PM) skills by putting those abilities immediately to use on their Startup Studio projects. In each session, students learn about a different aspect of product management, product design, or technology development, then practice applying it to their Startup Studio projects, working in the Studio with their project teams and with the help and critique of the practitioner instructors and sometimes visiting practitioners. By the end of the semester, students will have developed and practiced many of the fundamental product management skills required to develop new technology products, and their Startup Studio projects will have greatly benefited from the practice.

  • Leadership for Studio 1.0

    This course deals broadly with leadership – of teams, projects, products, businesses, and communities. The course will pay special attention to leadership in the context of digital transformation and its social and economic impacts. Students will learn effective team-building and teamwork strategies, communication and presentation skills, and best practices for building a collaborative, creative and open culture in the workplace. As part of a personal development process, toolkits and exercises will be provided to promote critical thinking and moral reasoning skills. Sessions on social and multicultural awareness and sensitivity will equip students to be global leaders in a digitally-transformed world. In short, this course will cover all aspects of how students can become leaders in a digital economy.

  • Conversations in the Studio 1.0

    This course features a weekly guest practitioner for a provocative, closed-door discussion with students. The guest practitioners are active entrepreneurs, intrapreneurs, social entrepreneurs, engineers, designers, artists, VCs, lawyers, writers, ethicists, and other diverse leaders who are affecting society through their entrepreneurial efforts. Conversations take place in the Cornell Tech Studio and are moderated each week by a randomly assigned group of students who come prepared with questions and discussion topics. This is not a lecture: it's a weekly wake-up call.

Technical Courses

Applied Machine Learning 3.0 CS 5785

This course will help students learn and apply key concepts of modeling, analysis and validation from Machine Learning, Data Mining and Signal Processing to analyze and extract meaning from data. Students will implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. They will also gain a working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, association rule mining and dimensionality reduction.

Bayesian Estimation & Learning 3.0

Computer Vision 3.0 CS 5760

The goal of computer vision is to compute properties of the three-dimensional world from digital images. Problems in this field include reconstructing the three-dimensional shape of an environment, determining how things are moving, and recognizing people and objects and their activities, all through analysis of images and videos. This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation and mobile computer vision. This is a project-based course, in which students will implement several computer vision algorithms throughout the semester.

Data Science in the Wild 3.0 CS 5304

Companies and organizations collect massive amounts of data, and the task of a data scientist is to extract actionable knowledge from the data – for scientific needs, to improve public health, to promote businesses, for social studies and for various other purposes. This course will focus on the practical aspects of the field and will attempt to provide a comprehensive set of tools for extracting knowledge from data.

Design Thinking 1.0 NBAY 5180

This hands-on course will prepare students to be future innovators by teaching Design Thinking, the human-centered design methodology pioneered by IDEO and Stanford d.school founder, David Kelley. Students will work on a team with peers from other disciplines so as to experience the importance of “radical collaboration.” All teams will work on the same challenge, and students will be asked to design an innovative solution to this complex problem.

Feedback Systems and Reinforcement Learning 3.0

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.

Law for Non-Lawyers (for non-LLM students) 1.0

This class introduces the principal legal issues involved in starting, managing and operating a technology-oriented business by entrepreneurs. It is intended to provide non-law students with an understanding of many of the laws and regulations to which developing businesses in the United States tech sector are typically subject—from the time an entrepreneur conceives and begins to build a business, implements a business plan, and obtains financing, to when she begins operations in anticipation of managing a mature company and considering possible exit strategies. The instructor, a former corporate partner in a large New York City law firm, will adopt the role of a general counsel to a start-up company advising his client/students about how laws and regulations affect their businesses at various stages of development, as well as about typical key contractual terms and negotiating strategies. Practicing lawyers will serve as guest lecturers. The course is designed to impart an understanding not only about substantive areas of the law that intersect with tech businesses but also about the role that lawyers should—and should not—play in burgeoning business enterprises. Students will gain insights into how lawyers approach business problems and the benefits and limitations of such a perspective.

Natural Language Processing 3.0 CS 5740

This course constitutes an introduction to natural language processing (NLP), the goal of which is to enable computers to use human languages as input, output, or both. NLP is at the heart of many of today's most exciting technological achievements, including machine translation, automatic conversational assistants and Internet search. Possible topics include summarization, machine translation, sentiment analysis and information extraction as well as methods for handling the underlying phenomena (e.g., syntactic analysis, word sense disambiguation, and discourse analysis).

Optimization Methods 3.0 ORIE 5380

This course covers algorithmic and computational tools for solving optimization problems with the goal of providing decision-support for business intelligence. We will cover the fundamentals of linear, integer and nonlinear optimization. We will emphasize optimization as a large-scale computational tool, and show how to link programming languages, such as Python, Java and C++, with optimization software, such as Gurobi and CPLEX, to develop industrial-strength decision-support systems. We will demonstrate how to incorporate uncertainty into optimization problems. Throughout the course, we will cover a variety of modern applications and show how to deploy large-scale optimization models.

Physical Computing 3.0 CS 5422

This course provides a hands-on introduction to the resources for designing and fabricating smart systems using hardware components including sensors and sensor networks, analog instrumentation, embedded digital processing (microcontroller programming such as the Arduino system), graphics and I/O chips, flash memory, wired and wireless communications, PCB layout and fabrication, 3-D printing, and laser cutting. The key characteristics of the components and their interfaces will be presented. Using these tools, small multidisciplinary groups will conduct a hardware project of their choice.

Signal and Data Processing 3.0

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.

Studio & Interdisciplinary Courses

Business for Tech 1.0 NBAY 5500

Business for Technologists (BT) is a comprehensive introduction to the key aspects of new product development and product management that will provide helpful perspective for technologists working in cross functional teams with business leaders in the marketing, product, finance, legal and business development disciplines. BT focuses both on concepts and frameworks utilizing “solutions architecture” while working on cross functional teams in both agile and waterfall development environments. Students will utilize their team projects as the basis to complete customer development, market segmentation, product placement, construct business models and define “proof points” and milestones that lead to successful outcomes for teams comprised of both technologists and subject matter experts from a broad array of business disciplines. The course will also discuss the development of an “intellectual property suite” and its importance in the new product development process and relevance to the competitive landscape. While the course focuses on scalable businesses, the principles apply to businesses of all sizes. Topics covered will include definition of the market, sales and distribution, competition, business development, project management and milestone based performance tracking. The course culminates in a capstone project of writing a launch plan for a new technology product’s market test.

Conversations in the Studio 1.0 CS 5091

This course features a weekly guest practitioner for a provocative, closed-door discussion with students. The guest practitioners are active entrepreneurs, intrapreneurs, social entrepreneurs, engineers, designers, artists, VCs, lawyers, writers, ethicists, and other diverse leaders who are affecting society through their entrepreneurial efforts. Conversations take place in the Cornell Tech Studio and are moderated each week by a randomly assigned group of students who come prepared with questions and discussion topics. This is not a lecture: it's a weekly wake-up call.

Leadership for Studio 1.0 NBAY 6800

This course deals broadly with leadership – of teams, projects, products, businesses, and communities. The course will pay special attention to leadership in the context of digital transformation and its social and economic impacts. Students will learn effective team-building and teamwork strategies, communication and presentation skills, and best practices for building a collaborative, creative and open culture in the workplace. As part of a personal development process, toolkits and exercises will be provided to promote critical thinking and moral reasoning skills. Sessions on social and multicultural awareness and sensitivity will equip students to be global leaders in a digitally-transformed world. In short, this course will cover all aspects of how students can become leaders in a digital economy.

Product Management 1.0 CS 5093

This studio-based course helps students learn about and develop product management (PM) skills by putting those abilities immediately to use on their Startup Studio projects. In each session, students learn about a different aspect of product management, product design, or technology development, then practice applying it to their Startup Studio projects, working in the Studio with their project teams and with the help and critique of the practitioner instructors and sometimes visiting practitioners. By the end of the semester, students will have developed and practiced many of the fundamental product management skills required to develop new technology products, and their Startup Studio projects will have greatly benefited from the practice.

Product Studio 3.0 CS 5999

In Product Studio you and a team of your classmates will respond to a "Company Challenge" by developing, testing, and presenting a new product or business idea. Previous challenges have been posed by organizations as diverse as the Robin Hood Foundation, Uber, Weight Watchers and Bloomberg.

Startup Ideas 1.0 CS 5092

This studio-based course helps students develop their ability to imagine, recognize, develop and improve startup ideas. In each class, students learn a different approach to product ideation or product critique, then practice that approach, working in many different teams -- often with the advice of visiting entrepreneurs, VCs, domain experts, and other practitioners. Students invent and explore hundreds of startup ideas, and help each other evaluate and improve those ideas. By the end of the course, students self-organize into co-founding teams around specific startup ideas that they will pursue in Startup Studio the following semester.