Cornell Tech Course Planner

An introductory course on robot perception techniques for modeling, fusing, and interpreting heterogeneous and dynamic sensor measurements in the context of robot motion and uncertain environments. The course covers sensor modeling, artificial vision, acoustic sensing, and probabilistic filtering methods. Emphasis is placed on intelligent sensor fusion, object detection and classification, tracking, localization and mapping, exploration, and information-driven motion planning. Algorithms inspired by neural networks, Bayesian networks, graphical models, and information theory are examined. Students investigate perception-driven decision making through benchmark problems such as coverage, target search, tracking, and pursuit-evasion. Applications are drawn from environmental monitoring, surveillance, sensing-and-pursuit games, and human-robot interaction.

This course explores the behavioral foundations of communication technology and the information sciences, and the ways in which theories and methods from the behavioral sciences play a role in understanding people's use of, access to and interactions with information and communication technologies.

This course covers the algorithms and data structures that are fundamental to modern large-scale applications. We will cover a range of techniques including advanced graph algorithms, hash tables, vector search, and streaming and sketching algorithms. Applications will include selected topics in storage and memory systems and machine learning.

This course covers the human-centered and technical workings behind interactive devices ranging from cell phones and video game controllers to household appliances and smart cars. This is a hands-on, lab-based course. For the final project, students will build a functional IoT prototype of their own design, using Python, single-board Linux computer, embedded microcontrollers, and/or other electronic components. Topics include electronics prototyping, interface design, sensors and actuators, microcontroller development, physical prototyping, and user testing.

This course presents an introduction to virtual and augmented reality technologies, with focus on fundamental principles from 3D math, human perception, graphics, and interaction. Concepts from the contributing fields of computer vision, computer graphics and human computer interaction will be introduced in the context of virtual and augmented reality. Students will be tasked with creating their own virtual or augmented reality application as a course project.

Human-Computer Interaction (HCI) and design theory and techniques. Methods for designing, prototyping, and evaluating user interfaces. 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 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 with optimization software 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, including pricing and marketing for e-commerce, ad auctions on the web, and on-line ride-sharing.

This Master's level course is designed to provide a hardware-centric overview of computer systems used in modern computing platforms. From the bottom up we will study the architecture of processor architectures (e.g., pipelined CPUs, ISA, RISC vs. CISC, out-of-order execution) and memory systems (e.g., memory hierarchy, caching, DRAM memories). We will understand how to evaluate the performance of modern processors and exploit parallelism in applications. This includes parallelization across multi-core CPUs, GPUs, and specialized hardware. Through ands-on assignments and an open-ended project students will develop a holistic understanding of modern computer systems and how they are designed.

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, and dimensionality reduction.

Students will learn deep neural network fundamentals, including, but not limited to, feed-forward neural networks, convolutional neural networks, network architectures, optimization methods, practical issues, recurrent neural networks, transformers, generative models, foundation models, current limitations of deep learning, and visualization techniques. We still study applications to problems in computer vision and to a lesser extent other domains such as natural language and audio processing.