MAT 5983 Neural Networks for Data Analysis
Time: 6:00pm – 7:30pm MTWRF MS 2.01.06 Main Campus Instructor: Dr. Richardson
Machine Learning and Artificial Neural Networks are hot topics in any area requiring Big Data analytics, including image processing, finance, and cybersecurity. This course explores the mathematics of artificial neural networks (ANNs). Topics include perceptrons, Support Vector Machines, decision trees, gradient descent & genetic algorithms for minimizing cost/energy functionals, convolutional NNs, and deep belief networks. Software for ANNs will be introduced – Keras, Theano, H20, Rapidminer, and Google’s Tensorflow. Project datasets include UC Irvine repository, PCAP files of network traffic, examples from SMART Grid/IoT. Prerequisites: Graduate standing, math through calculus, familiarity with Python or R. Text: Python Machine Learning – S. Raschka, PACKT; Ref. Text: Matrix Methods in Data Mining and Pattern Recognition – L. Elden, SIAM. Syllabus available at bluebook.utsa.edu.