What Your Schedule Might Look Like
- Fall Semester
- Spring Semester
- Program Credits 12
- Project & Interdisciplinary Credits 3–4
- Semester total 15–16
- Technical Credits 12
- Project & Interdisciplinary Credits 3–4
- Semester total 15–16
- Technical Credits
- Project & Interdisciplinary Credits
- Semester total 0
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. Implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. Gain working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, association rule mining, and dimensionality reduction.
Massive amounts of data are collected by many companies and organizations 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.
Human-Computer Interaction (HCI) and design theory and techniques. Methods for designing, prototyping, and evaluating user interfaces to computing applications. Basics of visual design, graphic design, and interaction design. Understanding human capabilities, interface technology, interface design methods, prototyping tools, and interface evaluation tools and techniques.
This course covers the analysis of data for making decisions with applications to electronic commerce, AI and intelligent agents, business analytics, and personalized medicine. The focus will be on learning good and automated decision policies, inferring causal effects of potential decisions, and interactive and intelligent systems that learn through acting and act to learn. Topics include A/B testing, sequential decision making and bandits, decision theory, risk minimization and generalization, Markov decision processes, reinforcement learning, analysis of observational data, instrumental variable analysis, and algorithmic fairness of personalized decision policies. Students are expected to have taken a first course in machine learning and have working knowledge of calculus, probability, and linear algebra as well as a modern scripting language such as Python.
In this course, we will learn how to model randomness, analyze its impact and make optimal decisions when it is present. We will cover stochastic modeling techniques, statistical principles, simulation, and decision-making under uncertainty. Using applications, we will demonstrate how we can use statistical principles to gain insight from data generated by systems with randomness. We will use simulation models to assess the performance of such systems and gauge how it changes in response to our decisions. We will introduce and use stochastic modeling techniques, such as Markov chains and Brownian motion, to build models of random phenomena and use these to gain understanding and guide decisions. As well as covering theoretical concepts, the course will put substantial emphasis in computational implementation of both simulation and decision-making problems.
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).
The course examines how the computing, economic and sociological worlds are connected and how these connections affects these worlds. Tools from computer science, game theory and mathematics are introduced and then used to analyze network structures present in everyday life. Topics covered include social networks, web search, auctions, markets, voting, and crypto-currencies (e.g. bitcoin).
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.
This course covers online and physical service systems with a focus on designing and managing them. When designing service systems, we need to determine the sales channels to use when offering services, design incentive mechanisms and make tactical capacity decisions. When operating service systems, we need to make rea-time pricing decisions, allocate capacity between different needs, make product recommendations, and forecast demand and customer behavior. Depending on the application setting, the capacity we manage can be physical, such as seats on an airplane to be allocated to passengers with different willingness to pay amounts, or digital, such as visitors on a webpage to be allocated between advertisers. We will cover ideas from revenue management, experimentation for demand learning, auctions, mechanism design and network theory.
Project & Interdisciplinary Courses
Successfully innovating inside of a large company takes a new set of skills. In BigCo Studio, you will learn how to build products in a complex environment at scale and navigate business development, M&A, and other corporate activities to drive strategic initiatives within large companies. Working in teams, you’ll be matched with a C-suite or VP advisor from a real BigCo to research, prototype, and present a new product that helps the company achieve its mission.
This course introduces students to fundamental concepts in digital marketing and prepares them for roles as a marketer, entrepreneur or product manager. Students will be exposed to an overview of the major players in the advertising and digital industries, as well as a variety of tools commonly found in start-ups and technology firms. Course material will be covered with a mixture of case studies, lectures, and guest speakers.
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.
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 is the foundational studio course for product development at Cornell Tech. Students form semester-long teams and select a “How Might We” question posed by a company. During the semester students learn the basics of product development so they can apply the knowledge and skills from their degree program: identifying impactful problems to solve, product ideation and design, development process, and constructing a meaningful product narrative and complete product loop. Students present their working product, narrative, and thought process four times during the semester, after completing each of three 24-hour “studio sprints” where they will focus on developing their product and a final product presentation at the end of the semester.
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.