Lunch will be served at 11:45 AM.
My talk will be divided into two phases: a teaching demo and a talk on my teaching pedagogy. For the teaching demo, I will give an overview of machine learning covering common terms, categories, data processing, and validation. For the talk, I will go over my teaching pedagogy for computing education. I will cover the challenges faced by students in Computer Science and how we can help our students overcome these challenges. I will also talk briefly about my teaching experience and my vision for the future.
Trevor Bonjour is a Ph.D. Candidate in Computer Science at Purdue University. His research focuses on developing reinforcement learning techniques to build agents capable of detecting and adapting to novel situations (unseen during training) in multi-agent environments. Previously, he earned his master’s degree in Computer Science from Johns Hopkins University, where he worked on Causal Inference. Prior to that, Trevor worked as a Software Engineer for five years. Along with his Ph.D., he is working towards the Teaching and Learning in Engineering Graduate Certificate from the School of Engineering Education at Purdue.
Department of Computer Science