MLCS61001 - Machine Learning for Cybersecurity
Course Description
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.
In this 5 week course, you will develop the technical skills necessary to learn how to deploy data-driven prevention strategies using machine learning and other innovative solutions. Faculty will teach cutting-edge cybersecurity methods using real-world case studies and datasets, building both fundamental and practical knowledge. Information security managers, engineers, and professionals whose role includes working in applied computer security or cybersecurity are encouraged to enroll.
Prior experience with machine learning is not required.
Program Format and Prerequisites
This certificate is offered remotely with live online classes. Your program experience will include:
- Live Class Sessions: The primary focus of the live-online class sessions will be to discuss real-world cases and hands-on group activities.
- Weekly Self-Paced Coursework: Weekly pre-recorded lectures will be provided for all modules. The length of these recordings will be from 30 to 60 minutes long.
- Capstone Case Study: Students will have the opportunity to develop a real or hypothetical cybersecurity machine learning deployment case study, culminating in personalized UChicago faculty feedback and guidance on your strategy.
- Optional Networking and Offices Hours: Connect with peers in your industry through virtual happy hours and discuss your cybersecurity strategy with instructors through virtual office hours.
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