Cornell Tech Course Planner

This course prepares students to tackle the technical challenges to designing and operating smart and dynamic infrastructure systems. In particular, students will learn to combine data and models to control overall system performance in the face of uncertainty. The class will focus on smart city infrastructure systems that are self-aware, with continual surveillance of the built and natural environment and an autonomous capacity to control resource allocation. This course will build upon fundamental engineering principles (for systems such as transportation, energy, and water resources) and teach students to employ emerging sensor technologies, accompanying data analytics, resource demand forecasting, and model predictive control theory. Students will learn to couple engineering models of infrastructure with data-driven probabilistic models of resource demand and the approaches to control these integrated hybrid systems for optimal and equitable resource allocation with improved resilience to exogenous disturbances. Finally, the class will explore cases studies in urban flooding, energy supply, transportation and air quality, and water supply.

This course explores key theoretical and methodological approaches underlying the study of information, technology, and society, focused primarily (though not exclusively) on social science approaches-drawing from disciplines like sociology, communications, history, science & technology studies, and others. The course is designed to be rigorous and to prepare students to make their own analytically and theoretically sound contributions to scholarship about information, technology, and society.

This course in Discrete Optimization is focused on Nondeterministic Polynomial-hard problems but with a very strong focus on the use of Mixed-Integer Linear Programming, general-purpose solvers to attack them.

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.The course will cover the topics needed to solve data-science problems, which include problem formulation (business understanding), data preparation (collection, sampling, integration, cleaning), data modeling (characterization, model selection, and analysis), implementation (large-scale data processing, feedback loops, QA) and communication (data presentation, visualization). Advanced topics such as causal inference and processing streaming data will be presented.Throughout the course, the students will perform a data-science mission with all the required steps, from problem formulation to result presentation.

Machine learning is increasingly used in both high-stakes and everyday settings. Yet, its impacts are uneven, often reinforcing existing social hierarchies by disproportionately benefiting some groups while harming others. This course introduces multiple perspectives on fairness in machine learning, spanning domains in predictive and generative AI as well as tabular, image, and text data. Students will develop the skills to identify fairness challenges, understand why they are difficult to address, and reason through strategies for approaching them.

Viewed variously as a niche currency for online criminals and a technological threat to the financial industry, Bitcoin has fueled myth-making and financial speculation, as well as real innovation. In this course, we will study not just Bitcoin, but the rich technological landscape it has inspired and catalyzed, especially smart contracts and Web3. Topics will include: the mechanics of consensus algorithms and their role in blockchains and cryptocurrencies; cryptographic tools employed in cryptocurrencies, including hash functions, digital signatures, and zero-knowledge proofs; the evolution and mechanics of Bitcoin and its ecosystem; smart contracts; and special topics, such as trusted hardware in blockchain-based systems, crime, and NFTs. Grading will be based on homework assignments and a final exam.

This course constitutes an introduction to computational natural language modeling. Language modeling is at the heart of many of today’s most exciting technological achievements, including AI assistants, automated translation, autonomous research, coding agents, and Internet search. The course will introduce core problems and methodologies in language modeling, including machine learning techniques, model design, task formulation, and evaluation. The course covers the core techniques of the field, through both instruction and hands-on implementation.

This Master's level course will take a hardware-centric view of machine learning systems. From constrained embedded microcontrollers to large distributed multi-GPU systems, we will investigate how these platforms run machine learning algorithms. We will look at different levels of the hardware/software/algorithm stack to make modern machine learning systems possible. This includes understanding different hardware acceleration paradigms, common hardware optimizations such as low-precision arithmetic and sparsity, compilation methodologies, model compression methods such as pruning and distillation, and multi-device federated and distributed training. Through hands-on assignments and an open-ended project, students will develop a holistic view of what it takes to train and deploy a deep neural network.

An in-depth introduction to deep generative models. This course covers the mathematical foundations of generative models and their implementation as deep neural networks. Topics include diffusion models, variational autoencoders, generative adversarial networks, and network architectures for generation. These topics will be discussed in the context of applications in computer vision and natural language processing.

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).