teaching

Fundamentals of embedded systems, machine language execution, assembly and C language programming, local variables and subroutines, input/output synchronization, analog to digital conversion and digital to analog conversion, debugging, and interrupts. Students write software for embedded systems and understand how to test these systems. Topics around hardware and firmware design will be covered and applied to a game design project.

This NVIDIA DLI Workshop is about learning how to implement multiple AI-based approaches to solve a specific use case of identifying network intrusions for telecommunications. Prepare data and build, train, and evaluate models using XGBoost, Autoencoders, and GANs. Detect anomalies in datasets with both labeled and unlabeled data. Classify anomalies into multiple categories regardless of whether the original data was labeled.

This is an introductory course about fundamental concepts and methods applied in data science, focusing on energy applications. Participants will explore topics such as probability theory, probability distributions, statistical data modeling and inference, linear regression and predictive models, time series forecasting, dimension reduction, introduction to machine learning, etc.

ICPE 638

Participants will explore topics such as image classification, transfer learning, object detection, segmentation, and real-world projects. Students will gain practical skills in implementing algorithms such as ResNet, VGG, YOLO, DETR, Mask RCNN, and UNet.

ECEN 689

An introduction to the underlying concepts of computer vision and image understanding. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation.

CSCI 631