DATA 442 - Neural Networks & Deep Learning

Credits: (3)
Prerequisite(s): DATA 301 (formerly DATA 310) and recommended: (CSCI 241 or ANTH 454 or BIOL 325 or BIOL 327 or ECON 308 or GOVT 302 or MATH 351 or MATH 352 or SOCL 353)
College Curriculum: COLL 400
This course teaches the foundations of Neural Networks and Deep Learning. Students entering into this course should have, at minimum, a background in data preprocessing, cleaning, manipulation, and dimensionality reduction within python. Through an applied learning project, you will learn how to implement a machine learning project from design to implementation in the context of neural networks. Topics we will cover include the basic building blocks of neural networks, RNNs, convolutional networks and computer vision, backpropagation basics and strategies (including inductive transfer approaches), differences between technical implementations (i.e., TensorFlow, Keras, Torch), and more.