This course provides an introduction to human-centered design through a series of small user interface development projects covering information foraging, learnability, and problem solving. Projects will begin with reviewing a relevant model of human behavior. Each project will then provide an opportunity to explore how to apply that model in the design of a new user interface.

AI has made increasing inroads in a broad array of applications, many that have socially significant implications. This course will study a number of such applications, focusing on issues such as AI used for social good, fairness and accountability of AI, and potential security implications of AI systems.

The discipline of artificial intelligence (AI) is concerned with building systems that think and act like humans or rationally on some absolute scale. This course is an introduction to the field, with special emphasis on sound modern methods. The topics include knowledge representation, problem solving via search, game playing, logical and probabilistic reasoning, planning, dynamic programming, and reinforcement learning. Programming exercises concretize the key methods. The course targets graduate students and advanced undergraduates. Evaluation is based on written and programming assignments, a midterm exam and a final exam.

This course examines complex systems through the eyes of a computer scientist. We will use the representative power of graphs to model networks of social, technological, or biological interactions. Network analysis provides many computational, algorithmic, and modeling challenges. We begin by studying graph theory, allowing us to quantify the structure and interactions of social and other networks. We will then explore how to practically analyze network data and how to reason about it through mathematical models of network structure and evolution. We will also investigate algorithms that extract basic properties of networks in order to find communities and infer node properties. Finally, we will study a range of applications including robustness and fragility of networks such as the internet, spreading processes used to study epidemiology or viral marketing, and the ranking of webpages based on the structure of the webgraph. This course combines concepts from computer science and applied mathematics to study networked systems using data mining.

The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience. This course is a broad introduction to machine learning, covering the foundations of supervised learning and important supervised learning algorithms. Topics to be covered are the theory of generalization (including VC-dimension, the bias-variance tradeoff, validation, and regularization) and linear and non-linear learning models (including linear and logistic regression, decision trees, ensemble methods, neural networks, nearest-neighbor methods, and support vector machines.

This course is an introduction to modern cryptography, with an emphasis on its theoretical foundations. Topics will include one-way functions, pseudorandom generators, public key encryption, digital signatures, and zero-knowledge proofs.

Disciplines such as medicine, business, science, and government are producing enormous amounts of data with increasing volume and complexity. In this course, students will study the principles for transforming abstract data into useful information visualizations.

This course introduces students to quantum computing, which leverages the effects of quantum-mechanical phenomena to solve problems. The course begins with material from physics that demonstrates the presence of quantum effects. Mathematical abstractions of quantum gates are studied with the goal of developing the skill to reason about existing quantum circuits and to develop new quantum circuits as required to solve problems. Students will develop a quantum-computer simulator and make use of open simulators as well as actual devices that can realize quantum circuits on the Internet.

This course provides a broad introduction to machine learning and statistical pattern classification. Students will study theoretical foundations of learning and several important supervised and unsupervised machine learning methods and algorithms, including linear model of regression and classification, logistic regression, Bayesian learning methods, neural networks, nearest neighbor method, support vector machines methods, clustering methods and principal component analysis. Students will also learn to use Python programming language to implement learned models and methods to solve pattern classification problems.

This course involves a hands-on exploration of core OS abstractions, mechanisms and policies in the context of the Linux kernel. Readings, lecture material, studio exercises, and lab assignments are closely integrated in an active-learning environment in which students gain experience and proficiency writing OS code, as well as tracing and evaluating OS operations via user-level programs and kernel-level monitoring tools. Topics include: system calls, interrupt handling, kernel modules, concurrency and synchronization, proportional and priority-based scheduling of processes and threads, I/O facilities, memory management, virtual memory, device management, and file system organization.

A systematic study of the principles, concepts and mechanisms of computer programming languages: their syntax, semantics and pragmatics; the processing and interpretation of computer programs; programming paradigms; and language design. Illustrative examples are selected from a variety of programming language paradigms.

The theory of language recognition and translation is introduced in support of compiler construction for modern programming languages. Topics include syntactic and semantic analysis, symbol table management, code generation, and runtime libraries. A variety of parsing methods is covered, including top-down and bottom-up. Machine problems culminate in the course project, for which students construct a working compiler.

Secure computing requires the secure design, implementation, and use of systems and algorithms across many areas of computer science. Fundamentals of secure computing such as trust models and cryptography will lay the groundwork for studying key topics in the security of systems, networking, web design, machine learning algorithms, mobile applications, and physical devices.

This course will focus on reverse engineering and malware analysis techniques. Reverse engineering -- the process of deconstructing an object to reveal its design and architecture -- is an essential skill in the information security community. Although hackers often use reverse engineering tools to discover and exploit vulnerabilities, security analysts and researchers must use reverse engineering techniques to find what a specific malware does, how it does it, and how it got into the system.

A broad overview of computer networking. Topics include history, protocols, Hyper Text Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Name System (DNS), peer-to-peer (P2P), transport layer design issues, transport layer protocols, Transmission Control Protocol (TCP), User Datagram Protocol (UDP), TCP congestion control, network layer, Internet Protocol version 4 (IPv4), Internet Control Message Protocol (ICMP), Internet Protocol version 6 (IPv6), routing algorithms, routing protocols, Open Shortest Path First (OSPF), Routing Information Protocol (RIP), Border Gateway Protocol (BGP), datalink layer and local area networks carrier sense multiple access with collision detection (CSMA/CD), Ethernet, virtual local area networks (VLANs), Point-to-Point Protocol (PPP), Multi-Protocol Label Switching, wireless and mobile networks, multimedia networking, security in computer networks, cryptography, and network management.