[Seminar] Learning from Data
Wednesday, January 26, 2022
11:00 am - 12:00 pm
Microsoft 365 @cougarnet.uh.edu authentication required to join
This presentation discusses how some concepts in human learning impact machine learning. It describes general structures for standard approaches to machine learning. Then, identifies differences in types of machine learning: supervised, unsupervised and reinforcement. It illustrates the design of a simple machine learning algorithm such as 1D regression and how it can be extended to multidimensional inputs and outputs. The presentation finalizes with the sample implementation of one or two machine learning algorithms such Naïve Bayes and/or Decision Trees.
About the Speaker
Jesús Ubaldo Quevedo-Torrero is a clinical associate professor of Computer Science and Engineering at the University of North Texas (UNT) where he teaches graduate courses in Machine Learning, Information Retrieval and Database Systems.
Prior to UNT, Dr. Quevedo spent about 17 years at the University of Wisconsin– Parkside (UWP) in several capacities such as tenured associate professor, Director of the Cybersecurity graduate program, Director of the Information Technology Management graduate program, and Chair of the Computer Science department for 8.5 years. While at UWP, he developed the curriculum for a Data Science certificate program. He also taught multiple Computer Science courses that included Artificial Intelligence, Data Science, Computer Vision and Database Systems. Dr. Quevedo has also served as a Lecturer of Computer Science at the University of Houston, a visiting faculty at Ostfalia University in Germany, a professor at both the Universidad Autonoma de Guadalajara and ITESM- Guadalajara in Mexico.
Before getting his Ph.D. in Computer Science from the University of Houston, Dr. Quevedo was a K12 certified teacher in Math, Special Education, Computer Science, School Administration and Bilingual Education with teaching experience in elementary, middle, and high school. He has published some articles on learning theories, computer science education and recursive querying. He has also been involved in research grants related to the retention of underrepresented students in Computer Science.