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.

Contact department for description.

Contact department for description.

Provides graduate students with a broad sampling of new and cutting-edge research related to environmental conservation to help foster critical thinking and provide a more expansive view of natural resources research. Seminars will be given by departmental faculty and faculty from other departments, both on campus and from other institutions. The seminars will be designed for both students who plan a research career and those who plan a more applied path.

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.

This course 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. In addition to learning about basic concepts from BCT 420 (advanced 3D modeling, digital fabrication, and an overview of Autodesk Revit and Trimble SketchUp), students in this course are required to independently research various BIM-related topics and apply them through project work and presentations.

We are living in a world where everyday objects, such as smartphones, cars, TVs, and even refrigerators, are becoming smarter and constantly connected to each other to build, operate, and manage the physical world. This emerging paradigm, namely the Internet of Things (IoT), has great potential to impact how individuals live and work by providing a source of innovative decision making.

This course covers a broad range of advanced level topics in natural language processing. It is intended for graduate students in computer science who have familiarity with machine learning fundamentals. It may also be appropriate for computationally sophisticated students in linguistics and related areas. Topics include probabilistic models of language, computationally tractable linguistic representations for syntax and semantics, and selected topics in discourse and text mining. After completing the course, students should be able to read and evaluate current NLP research papers.

This course offers a broad introduction to the forensic investigation of digital devices. We cover the preservation, recovery, harvesting, and courtroom presentation of information from file systems, operating systems, networks, database systems applications, media files, and embedded systems. The primary goal of the class is to understand why and from where information is recoverable in these systems. We also cover relevant issues from criminology, law, and the study of privacy.

The design and analysis of efficient algorithms for important computational problems. Emphasis on the relationships between algorithms and data structures and on measures of algorithmic efficiency. Sorting (heapsort, mergesort, quicksort), searching, graph algorithms. Experimental analysis of algorithms also emphasized. Use of computer required.