Luddy Indianapolis

This course covers deep learning neural networks. Topics include logistic regression, feedforward networks, autoencoders, convolutional neural networks, recurrent neural networks, graph neural networks, deep generative models

This course covers the theory, design, and application of scientific and information visualization, including algorithm design and implementation.

This course focuses on developing mobile applications for modern platforms and introduces common tools and languages used.

Students employ human-machine interface design principles and practices as an innovation engine for Internet-of-things (IoT) ecosystems. Through design challenges, they develop and refine IoT interfaces and their business models, drawing on case studies and a review of the literature.

This course covers advanced programming techniques, including user-defined functions and types, recursion vs. iteration, and parameter-passing mechanisms. Students learn abstract data types and algorithms, programming style, object-oriented programming, and web programming.