Big Data, & Deep Learning for Electrical & Computer Engineering

Catalog description

This class will focus of how to extract actionable, non-trivial knowledge from unstructured, heterogenous, massive number of data sets using machine learning and deep learning techniques. On the tool's side, we will cover the basic systems and techniques to store large volumes of data and modern systems for cluster computing based on MapReduce patterns such as Hadoop MapReduce, Apache Spark, and Flink. FPGAs, GPUs, and neuromorphic processors with emphasis on edge, fog, and cloud computing architectures, industry, manufacturing communications, autonomous navigation systems, IoT, systems, remote sensing. Prerequisites: EECE 304 and EECE 306 . Cross-listed with ECEG 767 .