GPU Computing for Machine Learning Systems

1 Course Unit

In this course, we will explore massively parallel programming, specifically on graphics processing units (GPUs), with immediate application to machine learning (ML) and artificial intelligence (AI). We’ll first outline computational aspects of ML and connect parallel programming to common components of deep learning. You will gain proficiency in GPU programming basics through hands-on projects with industry best practices and tools, eventually building up to implementing components of modern neural models. Required Prerequisites: CIT 5930, CIT 5940, CIT 5950, and CIT 5960. Intermediate/advanced knowledge of C/C++. Intermediate knowledge of Linear Algebra and Calculus (in particular, differentiation) Note: MSE-DS Online students are waived from needing to complete CIT 5910, CIT 5920, CIT 5930, & CIT 5940 as pre- req requirements. MSE-AI Online students are waived from needing to complete CIT 5910, CIT 5920, CIT 5930, CIT 5940, CIT 5950, and CIT 5960 as pre-req requirements. __________________________ Recommended Prerequisites: ESE 5460 and CIS 5530. Familiarity with Unix/Linux. Please note: This course is designed with the expectation that students will need to spend up to $300 on cloud computing resources in addition to tuition. Any expenses beyond this amount are also the responsibility of the student. Prerequisite: CIT 5930 AND CIT 5940 AND CIT 5950 AND CIT 5960 1 Course Unit