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 discussion or project experience. You’ll interact with faculty and fellow participants as you learn and apply emerging technologies and concepts.
Summer 2022 courses in Applications of Machine Learning in Data Science and Software-Defined Networking (SDN) will include business case studies, discussions on method selection, and opportunities to interact with leaders from CU Boulder and the Boulder area tech community.
Who Should Attend?
Managers of tech groups or tech products – from any industry – seeking a greater understanding of contemporary and future technical methods and their application.
Tech professionals or others interested in keeping up with the latest developments.
Applications of Machine Learning in Data Science
Methods in machine learning evolved from the notion that given an influx of data, a well-defined algorithm can teach itself to identify patterns and make future predictions online.
This course will motivate the use and importance of machine learning with case studies and pinpoint the organization, software, and mathematical knowhow needed for your research and business teams to bring these methods on board and into your vocabulary.
This course provides participants with vendor-neutral foundational knowledge of the major domains of networking practices that support the theory and practice of software-defined networking (SDN).
The conceptual background of SDN architecture, definitions, and the future of the industry are explored with business cases and discussion. The course is designed to serve a variety of audiences, such as SDN Sales Engineers, IT Managers, Product Managers, and anyone interested in learning the SDN concepts and the future of networking.
Dr. Levi Perigo, Scholar in Residence, Computer Science, CU Boulder
Dr. Eric Keller, Associate Professor, Electrical, Computer, and Energy Engineering, CU Boulder; CTO and Co-Founder, Stateless Inc.
Dr. Murad Kablan, CEO and Co-Founder, Stateless Inc.
Kablan received his PhD in Computer Science at University of Colorado Boulder. His doctoral research uncovered an opportunity for commercialization of SDN technologies, and he co-founded Stateless, Inc., to help others build large, scalable interconnections.
Associate Professor, Electrical, Computer, and Energy Engineering, CU Boulder; CTO and Co-Founder, Stateless Inc.
Keller’s work in networking, security, and programmable infrastructure introduces new systems, algorithms, and abstractions to enable a more manageable network and computing infrastructure. He received his Ph.D. in Electrical Engineering at Princeton University.
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.
Scholar in Residence, Computer Science, CU Boulder
Perigo’s research is focused on software-defined networking (SDN), network functions virtualization (NFV) and next generation internetworking technologies. He is the founder and CEO of Raven Innovation. He has a PhD in Information Systems from Nova Southeastern University.
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.