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. For more information, fill out the form here.
Machine Learning for Cybersecurity
- Time: Tuesdays and Thursdays from 7 to 9 p.m. (Central Standard Time)
- Dates: November 10, 12, 17, and 19, 2020
- Length: 4 days
- Cost: $3,000 (corporate group discounts available)
- Deadline: November 2
View the full program schedule.
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.
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
- 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:
- 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