This is a project based course, focusing on the science connecting the field of neural network with human brain mechanism, as well as advancements which are at the front of the field. We start by introducing a few neural network architectures with their learning paradigms, including deep feed-forward and recurrent networks, Hopfield's memory, and Kohonen's self organizing maps.

Nowadays, wireless technologies (such as 802.11 Wi-Fi) do not only provide data service but also cater to diverse applications including indoor localization, user authentication, contactless activity sensing, vital sign monitoring, gesture recognition, sleep sensing, wireless charging, etc. This course introduces the students with fundamentals in wireless networking and also the state-of-the-art sensing applications in the Era of Internet-Of-Things.

Randomness has proven itself to be a useful resource for developing provably efficient algorithms and protocols for large scale data processing. As a result, the study of randomized algorithms has become a major research topic in recent years. This course will explore a collection of techniques for effectively using randomization and for analyzing randomized algorithms, as well as examples from a variety of settings and problem areas. The course is a natural follow on both COMPSCI 514: Algorithms for Data Science and COMPSCI 611: Advanced Algorithms.

This is an advanced course in HCI. This course will provide a deeper treatment of some topics that are typically found in an undergraduate HCI course. For example, design methodologies, evaluation methodologies (both quantitative and qualitative), human information processing, cognition, and perception. This course will also introduce students to research frontiers in HCI. The course will cover topics of Universal Usability, CSCW, Digital Civics and fundamentals of designing interactive technology for people.

This course will cover Bayesian and Markov networks and their dynamic and relational extensions; exact and approximate inference methods; estimation of both the parameters and structure of graphical models.

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.

An in-depth examination of principles of distributed systems and advanced operating systems. Topics include client-server programming, distributed scheduling, virtualization and cloud computing, distributed storage, IoT. Familiarity with an undergraduate course on operating systems (COMPSCI 377 or equivalent) is helpful.

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.

This class is an in-depth introduction to systems, focusing on principles of system design that cross-cut numerous systems artifacts, including operating systems, databases, runtime systems, and architecture. We will cover all levels of the "system stack", from chips to distributed systems. This course may be used to satisfy systems core requirements.

The design and analysis of efficient algorithms for important computational problems. Paradigms for algorithm design including Divide and Conquer, Greedy Algorithms, Dynamic Programming; and, the use of Randomness and Parallelism in algorithms. Algorithms for Sorting and Searching, Graph Algorithms, Approximation Algorithms for NP Complete Problems, and others. Prerequisites: The mathematical maturity expected of incoming Computer Science graduate students, knowledge of algorithms at the level of COMPSCI 311.