Fall 2018: Computational Semantics (CSCI 2952-D)

Natural language understanding is a holy grail of AI. And with the machine learning advancing at such a rapid pace, breakthroughs in automatic language understanding seem to be just around the corner. But what exactly are the current barriers in automating human-like language capabilities? This course will dissect what makes language understanding so challenging, including both theoretical aspects (logic, formal semantics, pragmatics, knowledge representation) and practical methods (graphical models, game theory, neural networks). The course will be project-based, and will emphasize reading and critiquing current research in computer science, linguistics, and cognitive science.

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Spring 2019: Data Science (CSCI 1951-A)

Data is the new soil of business and (soon) the core of essentially all domains from material science to healthcare. Mastering big data requires a set of skills spanning a variety disciplines, from distributed systems to statistics to machine learning. It is essential to develop a deep understanding of a complex ecosystem of tools and platforms, as well as the communication skills necessary to explain advanced analytics. This course will provide an overview of the wide area of data science, with a particular focus on to the tools required to store, clean, manipulate, visualize, model, and ultimately extract information from large amounts of data. Topics include: Relational algebra and SQL, Data integration and cleaning, Data modeling in Python and Pandas, Visualizations using D3, Clustering and classification, Scaling ML algorithms, Large-scale processing tools like Spark.

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