This is a project-based course in which students will explore using large-scale datasets and data analysis to address real-world societal problems in domains such as sustainability, health, and work with different techniques of data analysis and processing. Students will address problems of societal or industrial relevance. Each semester, the course will offer one or more real-world datasets and a selection of sample problems and students will define a project based on these datasets to address a real-word problem in a group setting. Students will collaborate in groups for their project.

The course provides an introduction to machine learning algorithms and applications, and is intended for students with no prior experience with machine learning. Machine learning algorithms answer the question: 'How can a computer improve its performance based on data and from its own experience?' The course is roughly divided into thirds: supervised learning (learning from labeled data), reinforcement learning (learning via trial and error), and real-world considerations like ethics, safety, and fairness.

The Course explores key concepts of artificial intelligence, including state-space and heuristic search techniques, game playing, knowledge representation, automated planning, reasoning under uncertainty, decision theory and machine learning. We will examine how these concepts are applied in the context of several applications.

The design and operation of modern computer operating systems. Review of capabilities of typical computer hardware. Topics include command language interpreter (the shell), processes, concurrency, inter-process communication, linking and loading, memory management, transactions, file systems, distributed systems, security, and protection. Programming projects in C.

The design and operation of modern computer operating systems. Review of capabilities of typical computer hardware. Topics include command language interpreter (the shell), processes, concurrency, inter-process communication, linking and loading, memory management, transactions, file systems, distributed systems, security, and protection. Programming projects in C.

This course introduces the fundamental concepts of two-dimensional (2D) and three-dimensional (3D) computer graphics. It covers the basic methods for modeling, rendering, and imaging. Topics include image processing, 2D and 3D modeling, 3D graphics pipeline, WebGL, shading, texture mapping, ray tracing, 3D printing. Throughout the class, students will learn algorhithmic ways to model the visual world, and write JavaScript programs with WebGL to implement various computer graphics algorithms.

This introductory computer vision class will address fundamental questions about getting computers to "see" like humans. We investigate questions such as -What is the role of vision in intelligence? -How are images represented in a computer? -How can we write algorithms to recognize an object? -How can humans and computers "learn to see better" from experience? We will write a number of basic computer programs to do things like recognize handwritten characters, track objects in video, and understand the structure of images.

This course provides an introduction to the principles and practice of computer and network security. A focus on both fundamentals and practical information will be stressed. The three key topics of this course are cryptography, privacy, and network security. Subtopics include ciphers, hashes, key exchange, security services (integrity, availability, confidentiality, etc.), security attacks, vulnerabilities, anonymous communications, and countermeasures.

Computing has become data-driven, and databases are now at the heart of commercial applications. The purpose of this course is to provide a comprehensive introduction to the use of data management systems within the context of various applications. Some of the covered topics include the relational data model, data retrieval, application-driven database design, schema refinement, implementation of basic transactions, database security.

This course is an introduction to personal health sensing and activity monitoring through mobile phones and on-body sensors and addresses several aspects including how to build the computational elements for developing such applications by leveraging various sensors on smartphones.