Provides an integrated approach to understanding the practice of engineering in the real world. Students research the life cycle of a major engineering project, new technology, or startup company from multiple perspectives: technical, economic, political, cultural. Emphasis on analyzing engineering artifacts, understanding documentation, framing logical arguments, communicating effectively, and working in teams.

Mathematical models of systems from observations of their behavior. Time series, state-space, and input-output models. Model structures, parametrization, and identifiability. Non-parametric methods. Prediction error methods for parameter estimation, convergence, consistency, andasymptotic distribution. Relations to maximum likelihood estimation. Recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; and bounded but unknown noise models. Robustness and practical issues.

A graduate-level introduction to artificial intelligence. Topics include: representation and inference in first-order logic; modern deterministic and decision-theoretic planning techniques; basic supervised learning methods; and Bayesian network inference and learning.

6.896ĺĘcovers mathematical foundations of parallel hardware, from computer arithmetic to physical design, focusing on algorithmic underpinnings. Topics covered include: arithmetic circuits, parallel prefix, systolic arrays, retiming, clocking methodologies, boolean logic, sorting networks, interconnection networks, hypercubic networks, P-completeness, VLSI layout theory, reconfigurable wiring, fat-trees, and area-time complexity.

6.895 covers theoretical foundations of general-purpose parallel computing systems, from languages to architecture. The focus is on the algorithmic underpinnings of parallel systems. The topics for the class will vary depending on student interest, but will likely include multithreading, synchronization, race detection, load balancing, memory consistency, routing networks, message-routing algorithms, and VLSI layout theory. The class will emphasize randomized algorithms and probabilistic analysis, including high-probability arguments.

Think Raku is an introduction to computer science and programming intended for people with little or no experience.

This aim of this book is not primarily to teach Raku, but instead to teach the art of programming, using the Raku language. After having completed this book, you should hopefully be able to write programs to solve relatively difficult problems in Raku, but my main aim is to teach computer science, software programming, and problem solving rather than solely to teach the Raku language itself.

Think Raku is a free book available under a Creative Commons license. Readers are free to copy and distribute the text; they are also free to modify it, which allows them to adapt the book to different needs, and to help develop new material.

This course is offered to graduates and addresses issues regarding ultrafast optics. Topics covered include: generation and propagation of ultrashort pulses (nano-, pico-, femto-, attosecond pulses) and linear and non-linear effects. Applications of the topic vary and include high precision measurements, nonlinear optics, optical signal processing, optical communications, and x-ray generation.

This course examines human-computer interaction in the context of graphical user interfaces. The course covers human capabilities, design principles, prototyping techniques, evaluation techniques, and the implementation of graphical user interfaces. Deliverables include short programming assignments and a semester-long group project. Students taking the graduate version also have readings from current literature and additional assignments.