Deep Learning

Catalog description

This course will offer a mathematical and practical perspective on artificial neural networks for machine learning. Students will learn about the most prominent network architectures, such as for example, feedforward, recurrent, convolutional, and attention based neural networks. This course will also teach students optimization and regularization techniques used to train them — such as back-propagation, stochastic gradient descent, dropout, pooling, and batch normalization. Connections to related machine learning techniques and algorithms will be explored. In addition to understanding the mathematics behind deep learning, students will have the opportunity to train neural networks for a wide range of real-world applications. Recommended background: Machine Learning (CS 4342), and knowledge of Linear Algebra (such as MA 2071) and Algorithms (such as CS 2223) Units: 1/3 Category: II