This graduate seminar course will cover recent developments in programming languages and systems, examining the latest research papers from top programming languages and systems conferences. Topics of interest include bug detection and correction, domain-specific languages, and emerging topics like concurrency on multicore architectures.

This graduate seminar course will cover recent developments in programming languages and systems, examining the latest research papers from top programming languages and systems conferences. Topics of interest include bug detection and correction, domain-specific languages, and emerging topics like concurrency on multicore architectures.

This course will cover Bayesian and Markov networks and their dynamic and relational extensions; exact and approximate inference methods; estimation of both the parameters and structure of graphical models.

This course introduces graduate students to basic ideas about conducting a personal research program. Students will learn basic methods for activities such as reading technical papers, selecting research topics, devising research questions, planning research, analyzing experimental results, modeling and simulating computational phenomena, and synthesizing broader theories. The course will be structured around three activities: lectures on basic concepts of research strategy and techniques, discussions of technical papers, and preparation and review of written assignments.

An in-depth examination of principles of distributed operating systems. Topics include processes and threads, concurrent programming, distributed interprocess communication, distributed process scheduling, shared virtual memory, distributed file systems. MACH. Familiarity with an undergraduate course on operating systems (CMPSCI 377 or equivalent) is helpful.

This course examines the structure of modern computer systems. We explore hardware and technology trends that have led to current machine organizations, then consider specific features and their impact on software and performance. These may include superscalar issue, caches, pipelines, branch prediction, and parallelism. Midterm and final exams, individual projects, homework, in-class exercises. Prerequisites: CMPSCI 535 or equivalent.

Introduces students to the principal activities involved in developing high-quality software systems. Topics include: requirements analysis, formal specification methods, process definition, software design, and risk management.

An advanced course in robotics that covers mechanisms (kinematics and dynamics), actuators, sensors, signal processing, feedback control, and signal processing. The target is to provide a general understanding of sensorimotor systems that can be related to machine learning and other disciplines. We will relate to biological systems whenever possible. Programming exercises will accumulate code for a simulated mobile manipulator and students will pit their "robots" against those of their classmates. pre-requisite: linear algebra, differential equations, and programming skills.

An in-depth introduction to the main models and concepts of the mathematical theory of computation, including computability, complexity, and logic. Prerequisites: Cmpsci 250 and 311, or equivalent.

Not available at this time