BZAN 8415 Deep Learning and AI

This course will introduce deep neural networks. Students will learn foundational concepts such as stochastic gradient descent and backpropagation and how they are used to train network models. Students will also gain practical experience using Python libraries (e.g., TensorFlow, Keras) to implement various deep learning architectures. Different types of artificial neural network architectures will be covered so that students will learn about classes of applications with an eye toward human-centered data science in this class. These include convolutional neural networks, recurrent neural networks, and long short-term memory networks. Students will learn how to choose different architectures depending on the tasks being addressed. STEM-Designated