Enhance your technical knowledge while you advance your career.
Tech Frontiers is the professional development program of the College of Engineering and Applied Science of the University of Colorado Boulder, offering short-form learning on contemporary topics in engineering. Through live sessions taught by CU faculty experts, Tech Frontiers courses offer a mixture of classroom content and hands-on project experience. You’ll interact with faculty and fellow participants as you learn and apply emerging technologies and concepts.
Summer 2021 courses in Data Science and Machine Learning will include a discussion of ethical issues in tech, a session with CU’s NSF-funded AI Institute for Student-AI Teaming, and opportunities to interact with leaders from CU Boulder and the Boulder area tech community.
Who Should Attend?
Managers of tech groups – from any industry – seeking a greater understanding of contemporary technical methods and their application.
Tech professionals or others interested in keeping up with the latest developments.
People considering enrolling in an advanced degree program and wishing to explore the topic first.
Best for those with a familiarity with Python and basic coding knowledge. A brief introduction to Python with supplementary materials will be accessible prior to the course.
July 12-13, 2021
In a world with more data available and more uses being made of it than ever before, the ability to collect, prepare, analyze and interpret data is essential to virtually all fields.
Our data science course will cover the key aspects of the data science life cycle. Participants will work in teams to apply the concepts learned in the course to real data sets.
Cox’s background is in applied and computational mathematics. Prior to teaching at CU Boulder, she worked as a research and development engineer for the Applied Research Laboratory (ARL) at Pennsylvania State University. Her research included the development of autonomously guided underwater vehicles. Cox holds an MS in applied and computational mathematics from Florida State University and an MS in applied mathematics from University of Colorado Boulder.
Assistant Professor, Computer Science
Kann’s research focuses on deep learning for natural language processing. In particular, she is interested in transfer learning, approaches for low-resource languages, and computational morphology. She received her PhD in computer science from the University of Munich, Germany.
Associate Professor, Applied Mathematics
Kilpatrick’s research leverages behavioral and neural recording data sets from humans and other animals to determine how they make decisions and learn across multiple timescales — validating mathematical models of Bayesian computation and recurrent neural networks. He received his PhD in Mathematics from the University of Utah.
Assistant Professor, Computer Science; Research Associate, Institute of Cognitive Science
Quigley’s research centers on the use of learning analytics techniques to build machine learning models of student activity and understanding in science classrooms. He received his PhD in Computer Science from CU Boulder, where he worked on the Inquiry Hub Research-Practice Partnership and the Chicago City of Learning project.
Professor and Department External Chair, Department of Computer Science
At CU Boulder, Schnabel has served as the computer science department chair, founding director of the ATLAS Institute, and vice provost for academic and campus computing and campus Chief Information Officer. He was previously CEO of ACM (Association for Computing Machinery) and dean of the School of Informatics and Computing at Indiana University.