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From Detection to Prevention: Machine Learning for Cybersecurity

Strengthen your cybersecurity strategy with hands-on machine learning training from the Center for Data and Computing at the University of Chicago.

Aerial view of NBC Tower in downtown Chicago

Many organizations now use machine learning in their operations but have not yet realized the potential of these approaches for cybersecurity. Researchers at the Center for Data and Computing (CDAC) at the University of Chicago develop and study data-driven methods for applied cybersecurity, including machine learning defenses against data breaches, fraud, and other threats. From identifying backdoors in neural networks to automatically detecting malware, stolen accounts, or network attacks, machine learning offers essential new protections for businesses and individuals.

Prior experience with machine learning is not required.

Machine Learning for Cybersecurity

  • Time: Tuesdays from 7 to 9 p.m. (Central Standard Time)
  • Dates: March 30, April 6, 13, 20, and 27, 2021
  • Cost: $3,000 (Tuition support may be available. Please contact us to learn more.)

Learning Objectives

By the end of this program, learners will be able to:

  • Understand basic concepts for statistical modeling, including principles for model selection for supervised and unsupervised learning tasks in the context of cybersecurity.
  • Select the most appropriate models for various cybersecurity scenarios, such as malware classification, botnet detection, and intrusion detection.
  • Detect and defend against adversarial attacks on machine learning models in cybersecurity settings at both training and test times
  • Identify and understand means of navigating legal and ethical challenges that emerge from gathering data about human subjects and using it to build machine-learning models

UChicago Faculty will teach cutting-edge cybersecurity methods using real-world case studies and datasets, building both fundamental and practical knowledge.

Program Format

This certificate is offered remotely with synchronous (live) and asynchronous delivery methods. Your program experience will include:

  • A remote format with highly interactive live sessions and group discussions
  • Pre-recorded content and follow-up materials
  • Interactive Jupyter notebooks to engage with real-world problems in a case-study format
  • Small group collaboration with a focus on project-based learning
  • Coaching and discussion sessions with faculty and industry peers

To be best prepared to succeed in this program, students should have basic familiarity with:

  • Programming: Proficiency with one or more programming languages such as Python/C/C++/MATLAB/Java/JavaScript 
  • Basic Probability and Statistics: You should know the basics of probabilities, gaussian distributions, mean, and standard deviation
  • Linear Algebra: You should be comfortable with matrix/vector notation and operations
  • Computer Security: Basic knowledge of cybersecurity or applied computer security

Apply Machine Learning to Your Cybersecurity Toolkit

Fill out the form to get more information about the program or inquire about corporate group discounts.


Actionable Frameworks

Develop a holistic approach to data science focused on foundational topics that can be applied to new and evolving situations across cyber risk management.

Real-world Context

Apply foundational techniques to contemporary case studies, complex datasets, and unique business challenges.

Engage with Experts

Learn from and network with leading faculty and colleagues across the science and technology sectors.

Designed For:

This certificate is designed for a variety of professionals working across cybersecurity, applied science security, and other roles such as:

Information Security Managers
DevOps Engineers
Software Developers
System Administrators

Tuition Support and Corporate Group Discounts

Corporate group enrollment discounts and tuition support are available. Contact Sayeeda Khan, Assistant Director of Enrollment Management, to learn more.

Featured Faculty

Nick Feamster

Nick Feamster

Neubauer Professor of Computer Science; Faculty Director, Center for Data and Computing

Nick Feamster is the Neubauer Professor in the Department of Computer Science and the College, and faculty director of the Center for Data and Computing.

His research applies large-scale Internet measurement and machine learning to address problems in Internet performance, security and privacy, censorship, and the Internet of Things. His work aims to make networks easier to manage, more secure, more available, and an overall better experience for consumers.

Nick is an ACM Fellow and is also a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) for his contributions to cybersecurity, notably spam filtering. Prior to joining UChicago, Prof. Feamster was a full professor at Princeton University, where he directed the Center for Information Technology Policy (CITP).

Yuxin Chen

Yuxin Chen

Assistant Professor, Department of Computer Science

Yuxin Chen is an assistant professor at the Department of Computer Science at the University of Chicago. Previously, he was a postdoctoral scholar in Computing and Mathematical Sciences at Caltech, hosted by Prof. Yisong Yue. He received his Ph.D. degree in Computer Science from ETH Zurich, under the supervision of Prof. Andreas Krause. He is a recipient of the PIMCO Postdoctoral Fellowship in Computing and Mathematical Sciences, a Swiss National Science Foundation Early Postdoc.Mobility fellowship, and a Google European Doctoral Fellowship in Interactive Machine Learning.

His research interest lies broadly in probabilistic reasoning and machine learning. Yuxin is currently working on developing interactive machine learning systems that involve active learning, sequential decision making, interpretable models and machine teaching. You can find more information in his Google scholar profile.

Blase Ur

Blase Ur

Neubauer Family Assistant Professor of Computer Science and the College

Blase Ur researches computer security, privacy and human-computer interaction. His focus is on helping users make better security and privacy decisions, and improving user experience within complex computer systems. Asst. Prof. Ur founded the UChicago SUPERgroup, an interdisciplinary research collective comprised of dozens of researchers who work on computer security, privacy and usability. He has also worked extensively on supporting users’ online privacy, as well as studying both privacy and interaction aspects of the Internet of Things.

He has received best paper awards from CHI, the 2016 USENIX Security Symposium, and the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. In addition, he has strong interests in teaching and K–12 outreach, particularly with the goal of broadening participation in computer science.

Remote Learning at the University of Chicago 

Our innovative remote format transcends the traditional online learning experience by incorporating synchronous sessions such as simulations, case study group work, and networking to asynchronous engagement with pre-recorded lectures.   

You will have the opportunity to engage with interdisciplinary data science experts from business to healthcare to public policy, as well as peers across sectors. 

Aerial view of NBC Tower in downtown Chicago

About the Center for Data and Computing

The Center for Data and Computing (CDAC) is an intellectual hub and incubator for data science and artificial intelligence research at the University of Chicago. CDAC catalyzes new discoveries by fusing fundamental and applied research with real-world applications. As the focal point for data science research on campus, CDAC engages leaders from industry, government, and academia through innovative events and partnerships to spark new collaborations and technological discoveries.

Empower researchers, industry, government, and the public with the information and tools to meet the opportunities and challenges of the data revolution. Join our November cohort today.