From studies on cannabis, machine learning in physics and astronomy, to ethics in an age of big data—new undergraduate courses at UBC Science will bring widely discussed topics from the real world into the classroom this school year.
Learning foundations of biology through cannabis
A new three-credit course, Biology of Cannabis (BIOL 348), focuses on the study of cannabis as a way to teach students about biological concepts such as photosynthesis and plant metabolism. Students will also learn the history of the cannabis plant and how it has been used by humans over time.
“Adult use of cannabis became legal in Canada in 2018,” notes Dr. Christine Goedhart, science education specialist in the Department of Botany at UBC. “Since then, cannabis has become more socially acceptable and increasingly integrated into mainstream cultural, social, health and economic institutions. As such, students will likely be coming into contact with cannabis at some point and will need to make informed decisions about how they choose to engage with it.”
Machine learning from atoms to stars (or galaxies)
There are many machine learning resources available online for avid data science and artificial intelligence enthusiasts--but the information available can be difficult for students to access. Machine Learning for Physics and Astronomy Data Analysis (PHYS 310) will provide curated datasets to help students not only learn about machine learning, but apply it specifically in a physics and astronomy environment.
“Machine learning has emerged as a new universal tool for data-driven research, standing side-by-side with calculus, statistics and numerical simulations,” says instructor Dr. Joerg Rottler, professor in the Department of Physics and Astronomy.
“Recent examples in include the search for the Higgs boson in high-energy particle collisions, finding and classifying galaxies in astronomy, or high-accuracy protein-folding predictions with AlphaFold. This new course recognizes these developments and provides hands-on training on some common algorithms in the field.”
How ethical is your use of data?
As the field of data science grows, so does the pressing need to explore the ethics behind how data are accessed, and how they are being used. Fairness, Accountability, Transparency and Ethics in Data Science (DSCI 430) is a new course designed to get students thinking about these topics and learn how to apply them practically to their coding. As students graduate from UBC and explore a variety of work involving data, being able to think critically about the ethics behind data science will have positive impacts on their careers and society.
“We have a more pressing need for preparing students to not only be able to deliver products in the field of data science, but also think about the long-term consequences of what they produce,” says Dr. Giulia Toti, assistant professor of teaching in the Department of Computer Science. “Unlike other courses that teach ethical discourse, this one will focus on the transferring of the knowledge on ethics into what the students are coding.”
Fairness, Accountability, Transparency and Ethics in Data Science (DSCI 430) begins this fall, while Machine Learning for Physics and Astronomy Data Analysis (PHYS 310) and Biology of Cannabis (BIOL 348) will start in term 2 of this school year.
Sarah Chen
chen@science.ubc.ca
