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Artificial Intelligence will radically change education. Through machine learning, data mining, analytics, robotics, and user models, AI will replace false learning boundaries (e.g., learning places, time, level of study); personalize learning; make learning instantly available to everyone; connect learners with partners; provide multi-media; and augment human learning ability. This seminar examines recent work in explanatory and tutoring systems, presents theories about digital teaching and learning, and describes how to deliver personalized teaching in online systems.

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.

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.

In-depth introduction to Artificial Intelligence concentrating on aspects of intelligent agent construction. Topics include: situated agents,advanced search and problem-solving techniques, principles of knowledge representation and reasoning, reasoning under uncertainty, perception and action, automated planning, and learning.

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.

The course will teach students algorithms that intelligently process, analyze and generate visual data. The course will start by covering 2D image and 3D shape representations, classification and regression techniques, and the fundamentals of deep learning. The course will then provide an in-depth background on analysis and synthesis of images and shapes with deep learning, in particular convolutional neural networks, recurrent neural networks, memory networks, auto-encoders, adversarial networks, reinforcement learning methods, and probabilistic graphical models.