Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

The course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to implement abstract data collections and their associated operations. Specific implementations include linked structures, recursive structures, binary trees, balanced trees, and hash tables. Algorithm analysis and asymptotic bounding of implementations is a major topic throughout the course. The topics covered in this course are fundamental to programming and are essential to further computer science courses.

The course introduces and develops methods for designing and implementing abstract data types using the Java programming language. The main focus is on how to implement abstract data collections and their associated operations. Specific implementations include linked structures, recursive structures, binary trees, balanced trees, and hash tables. Algorithm analysis and asymptotic bounding of implementations is a major topic throughout the course. The topics covered in this course are fundamental to programming and are essential to further computer science courses.

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.

Covers topics such as the relational model, query languages, database design; storage and indexing, query processing and optimization, transaction management; Big Data systems; information integration; data stream systems; database security.

Using computers for system design and decision making under uncertainty. Monte Carlo methods, modeling discrete-event stochastic systems, random number generation, time-advance mechanisms, output analysis, simulation-based optimization. This course counts as a CS Elective toward the CS major (BA or BS).

Using computers for system design and decision making under uncertainty. Monte Carlo methods, modeling discrete-event stochastic systems, random number generation, time-advance mechanisms, output analysis, simulation-based optimization. This course counts as a CS Elective toward the CS major (BA or BS).

This course is an introduction to personal health sensing and monitoring through mobile phones and on-body sensors and addresses several aspects including mobile devices and applications for health, sensor data quality and reliability challenges, interference of key feedback, and practical considerations such as battery lifetime.