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.

Register today!

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.

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Courses

Data Science

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.

Program Content

  • Data science project life cycle.

  • Data collection, management, curation, cleaning and visualization.

  • Introduction to methods in statistical inference and machine learning.

  • Ethical issues in data science.

  • Hands-on project experience utilizing real data sets.

  • University/community reception preceded by a short session about CU-based NSF-funded Institute for Student-AI Teaming.

Faculty

Rachel Cox, Instructor, Computer Science, CU Boulder

Dr. Zachary Kilpatrick, Associate Professor, Applied Mathematics, CU Boulder

Dr. Bobby Schnabel, Professor and Department External Chair, Computer Science, CU Boulder

Key Benefits

  • Gain exposure to the key technical elements of data science, including preparing and cleaning data sets, and the main methods used in analyzing data and when each should be applied.
  • Understand the data science project life cycle.
  • Gain hands-on project experience applying data science techniques to realistic data sets.
  • Improve awareness of important ethical issues in data science.
  • Network with other experienced professionals and with leading faculty and corporate leaders from the Boulder area.
  • In-person or online attendance.

Cost

$2000 - Two-day course, in person or live-virtual
$1600 - Early registration discount ends April 30
$1800 - Special pricing for CU alumni

  • Use code CU-ALUMNI at checkout, does not stack with any other offers. 

Cost includes parking and lunch each day, coffee and tea service and a welcome reception with members of the Front Range tech community.

Register Today!

Contact techfrontiers@colorado.edu for more information. 

Machine Learning

July 15-16, 2021

The capabilities and applications of machine learning have exploded in recent years. Machine learning now impacts a wide spectrum of areas and has become a key competitive advantage.

Our machine learning course will cover the key machine learning methods, including deep learning/neural networks, classification methods, and non-parametric methods.

Program Content

  • Non-parametric machine learning / k nearest neighbors.

  • Classification methods, including hidden Markov models.

  • Introduction to deep learning / neural networks.

  • Ethical issues in machine learning.

  • Hands-on project experience utilizing real data sets.

  • University/community reception preceded by short session about CU-based NSF-funded Institute for Student-AI Teaming.

Faculty

Dr. Katharina Kann, Assistant Professor, Computer Science, CU Boulder

Dr. David Quigley, Adjunct Professor, Computer Science, CU Boulder

Dr. Bobby Schnabel, Professor and Department External Chair, Computer Science, CU Boulder

    Key Benefits

    • Develop an understanding of the machine learning methods that are most used in current practice, including deep learning / neural networks, hidden Markov models, and k nearest neighbors.
       
    • Improve awareness of important ethical issues in machine learning.
       
    • Gain hands-on project experience applying machine learning techniques to realistic data sets.
       
    • Become familiar with the broad spectrum of machine learning methods and when it is most appropriate to use each of them.
       
    • Network with other experienced professionals and with leading faculty and corporate leaders from the Boulder area.
       
    • In-person or online attendance.

    Cost

    $2000 - Two-day course, in person or live-virtual
    $1600 - Early registration discount ends April 30
    $1800 - Special pricing for CU alumni

    • Use code CU-ALUMNI at checkout, does not stack with any other offers. 

    Cost includes parking and lunch each day, coffee and tea service and a welcome reception with members of the Front Range tech community.

    Register Today!

    Contact techfrontiers@colorado.edu for more information. 

    Faculty

    Rachel Cox

    Instructor, Computer Science

    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.

    Katharina Kann

    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.

    Zachary Kilpatrick

    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.

    David Quigley

    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. 

    Bobby Schnabel

    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.