Machine Learning & data science
Genevera Allen is a sought after educator in machine learning and data science. She has developed and taught a variety of machine learning courses as well as several novel experiential learning courses in data science. These include an interdisciplinary data science capstone program where students work on real-world challenges and data sets from a variety of industries and disciplines. Genevera has been recognized with several teaching awards at Rice University including a Curriculum Innovation Award.
Fall 2022
Introduction to Machine Learning (ELEC 478 / 578)
This course is an advanced introduction to concepts, methods, best practices, and theoretical foundations of machine learning. Topics covered include regression, classification, regularization, kernels, clustering, dimension reduction, decision trees, ensemble learning, and deep learning.
Data Science Courses Developed
Data Science Consulting (DSCI 415 / 515)
Students in this course advise clients from Rice and beyond in a data science consulting clinic, learn best practices in consulting, and gain exposure to a variety of real data science problems.
Data Science Capstone (DSCI 435 / 535)
In this course, students work on interdisciplinary teams to solve a real-world data science project with clients from a variety of industries and disciplines.
Learn more about data science courses offered by the D2K Lab and check out the impact student capstone teams have made here.
Education Publications:
G. I. Allen, “Experiential Learning in Data Science: Developing an Interdisciplinary, Client-Sponsored Capstone Program”, In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE), 2021.