Artificial Neural Networks

(3 Credit Hours) · Undergraduate

Artificial neural networks are a class of machine learning algorithms inspired by biological neural networks in the brain. In recent years, great strides in the theory and application of artificial neural networks have made them one of the most powerful and popular choices for many machine learning applications. This course will cover the underlying theory and practice of using neural networks for machine learning problems, beginning with simple networks for linear and logistic regression and building up to deep convolutional neural networks. Students will learn to build and train artificial neural networks in Python using the popular PyTorch software package. Students should be comfortable with linear algebra, calculus, and probability/statistics. Experience with Python will not be assumed, but some previous programming experience will be helpful.