Intermediate statistics illustrated using examples from ecology. Topics include ANOVA, linear regression (simple and multiple), correlation, logistic regression, continency tables and noparametric methods. Techniques discussed in lectures and applied in laboratories.

Provides graduate students with an opportunity to deepen their studies in Building Information Modeling (BIM), a concept that is at the heart of contemporary digital building planning and execution. This course also provides a thorough introduction into Autodesk Revit and covers applied topics in Trimble SketchUp, both industry-standard software in the AEC field.

This course introduces concepts related to the management of urbanized landscapes, focusing on what comprises the urban forest, its function as a natural system and the value of urban forests as an environmental and social catalyst. Examination of what makes up the urban forest, how these components function and the importance of sustainable urban natural landscapes will be undertaken.

Not available at this time

Not available at this time

Not available at this time

This class is an introductory course in machine learning designed for senior level undergraduates and graduate students. The class will briefly cover topics in regression, classification, mixture models, trees, neural networks, deep learning, and ensemble methods. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work.

This course provides an overview of the core design principles used in the holistic design of low power embedded computing systems. The topics for the course will include wireless and ambient energy harvesting, alternative energy storage technologies, low power radio design, efficient sensor data processing, and low power AI accelerators. The course will include a survey of papers from recent top-tier conferences and journals. Students will have the opportunity to design a low power embedded system for IoT, mobile health, or other HCI application and empirically evaluate its performance.

This course will provide a working knowledge of the key parameters of modern phased array antenna systems. Phased arrays are a key component in radars, wireless and satellite communications, remote sensing and imaging systems, and in scientific instruments such as radio telescopes. Students will learn to critically evaluate the performance of phased array antenna systems with emphasis on factors that are important for high-performance radar and communication systems such as scanning, sidelobe levels, gain, bandwidth, sensitivity, linearity, etc.

This course examines applications of Data Science in the planning and operations of decarbonized electrical power networks. The course covers (i.) basic power systems concepts for centralized and distributed electricity generation and storage (ii.) an introduction to relevant methods in geospatial data analysis and machine learning, and (iii.) trends affecting the electricity sector, with a focus on decarbonizing supplies and electrifying end-uses.