Deep Learning

3 credits · Graduate School

Also listed as CS 5267. Provides students with an understanding of conceptual and practical aspects of models and algorithms used in deep learning. Key topics covered in this course include: Basic mathematical tools and machine learning concepts used in deep learning; Modern practical deep feedforward, convolutional, and recurrent networks; Regularization for deep learning; Optimization for learning deep models; Practical design methods. Serves as repeat credit for CS 5267. No credit for students who have earned credit for CS 4267 or ECE 4467. SPRING. [3]