Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications. Credit not awarded for both CS 7641 and CS 4641/CSE 6740/ISYE 6740.

Recitation for CS 1371

Scientific Machine Learning explores integrating machine learning with scientific modeling to solve real-world engineering problems. Students engage in discussions, peer reviews, and team projects.

This class is an advanced undergraduate course on the principles of programming languages. We will study the foundations of different programming language paradigms from a design and implementation perspective. The topics we will study include: functional programming, lambda calculus, logic programming, type systems, Floyd-Hoare logic, object-oriented programming, and program verification.

Operating systems concepts, including multi-threading, scheduling, synchronization, communication, and access control. Projects will cover design and implementation of several operating systems components.

CS3451 offers a comprehensive introduction to the mathematical and programmatic foundations of computer graphics. The course covers a range of topics, including the mathematical foundations, GPU pipeline, shape representation, procedural modeling, physically-based simulation, character animation, shading, lighting, texturing, and realistic ray-tracing rendering. The coursework encompasses a blend of programming assignments, in-class quizzes, a mid-term project, and a final project. These projects involve developing an interactive application using OpenGL and a modern GPU rendering pipeline to create visually impressive images and animations. Starter codes and code snippets will be provided to support your development. The grading criteria balance the technical implementation and an in-person demonstration of your code, with an emphasis on creativity and the production of striking visual imagery.