Hands-on course teaching the fundamentals of electronics of electrical and mechanical prototyping.

Multidisciplinary perspectives on cognitive science. Interdisciplinary approaches to issues in cognition, including memory, language, problem solving, learning, perception, and action. Crosslisted with PST, PSYC, and ISYE 3790.

A study of computational and technological advancements in journalism with emphasis on technologies for developing new tools and their potential impact on news and information.

Introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification and scene understanding.

Information-processing theories of modeling and design; topics include design decision-making, problem-solving and learning, and knowledge-based modeling and design.

An introductory course on pattern classification and decision problems with applications to character recognition, image analysis, and speech recognition.

Structured knowledge representation; knowledge-based methods of reasoning and learning; problem-solving, modeling and design.

Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications.

We investigate algorithms for robots and complex systems that make intelligent decisions. Emphasis on the theoretical and empirical properties of classical, geometric, stochastic/dynamic planning.

Methodologies for designing systems that comprehend natural language. Topics include lexical analysis, parsing, interpretation of sentences, semantic representation, organization of knowledge, and inference mechanisms.

Examines the expressive possibilities of artificial intelligence techniques in computer games. Students learn AI programming techniques, and how they strongly interface with game design.

The measurement and quantification of information. These ideas are applied to the probabilistic analysis of the transmission of information over a channel along which random distortion of the message occurs.

Introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification and scene understanding.

Computational machine models and their language classes. Undecidability. Resource-bounded computations. Central complexity-theoretic concepts such as complexity classes, reducibility and completeness.

Structured knowledge representation; knowledge-based methods of reasoning and learning; problem-solving, modeling and design.

Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications.

This course will cover the fundamental underpinnings and practice of deep learning, including neural network architectures, optimization algorithms, and applications to perception and Artificial Intelligence.

This course introduces students to the real world challenges of implementing machine learning based strategies including the algorithmic steps from information gathering to market orders.

We investigate algorithms for robots and complex systems that make intelligent decisions. Emphasis on the theoretical and empirical properties of classical, geometric, stochastic/dynamic planning.

Methodologies for designing systems that comprehend natural language. Topics include lexical analysis, parsing, interpretation of sentences, semantic representation, organization of knowledge, and inference mechanisms.

Examines the expressive possibilities of artificial intelligence techniques in computer games. Students learn AI programming techniques, and how they strongly interface with game design.