This course is intended to cover information retrieval and other information processing activities, from an applied perspective. There will be numerous programming projects and quizzes. Topics will include: search engine construction (document acquisition, processing, indexing, and querying); learning to rank; information retrieval system performance evaluation; classification and clustering; other machine learning information processing tasks; and many more.

This course is intended to cover information retrieval and other information processing activities, from an applied perspective. There will be numerous programming projects and quizzes. Topics will include: search engine construction (document acquisition, processing, indexing, and querying); learning to rank; information retrieval system performance evaluation; classification and clustering; other machine learning information processing tasks; and many more.

Introduction to the efficient management of large-scale data. Principles for representing information in the relational model; query languages for analyzing and manipulating data (SQL and others); core systems principles for data management (file organizations, query optimization, indexing, distributed data processing, concurrency control) and distributed data processing paradigms.

There is no better way to learn and deeply understand sustainability in the built environment than to experience this as a part of a real project. This course explores the concept of using living labs to accelerate innovation in the realm of low carbon approaches to buildings, materials, technologies, policies and ways of living.

This is not available at this time

This course will introduce students to the field of mobile sensing and ubiquitous computing (Ubicomp) - an emerging CS research area that aims to design and develop disruptive technologies with hardware and software systems for real-world messy, noisy and mobile scenarios. The students will learn how to build mobile sensing systems, how to implement it with ubiquitous computing tools, how to make sense of the sensor data and model the target variables.

This course will introduce students to the field of mobile sensing and ubiquitous computing (Ubicomp) - an emerging CS research area that aims to design and develop disruptive technologies with hardware and software systems for real-world messy, noisy and mobile scenarios. The students will learn how to build mobile sensing systems, how to implement it with ubiquitous computing tools, how to make sense of the sensor data and model the target variables.

In-depth exploration of techniques and materials including zone system, large format, and non-silver processes. Slide lectures, discussions, and readings. Prerequisite: ART 230 or consent of instructor.

This course is intended to provide biomedical engineering students with an understanding of the principles and devices of biomedical instrumentation with emphases on analog and digital electronic circuits, transducers, instruments, and measurements for obtaining information from human body and/or biological systems.