Machine Learning for Cybersecurity
offered by University of Chicago

Private University
217 acre campus
Estd. 1890

Machine Learning for Cybersecurity
at
University of Chicago
Overview

Evolve your cyber risk strategy with advanced training in machine learning for cybersecurity from the center for data and computing

Duration	5 weeks
Total fee	₹2.50 Lakh
Mode of learning	Online
Official Website	Go to Website
Course Level	UG Certificate

Machine Learning for Cybersecurity

Table of content

Overview
Highlights
Course Details
Curriculum
Faculty
Placements
Entry Requirements

Machine Learning for Cybersecurity
at
University of Chicago
Highlights

Earn a certificate from the University of Chicago
Learn from industry experts

Machine Learning for Cybersecurity
at
University of Chicago
Course details

Skills you will learn

Information Security Networking Security Cyber Security

Who should do this course?

For individuals who want to enhance their knowledge & skills in the field

What are the course deliverables?

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

More about this course

In this four-day certificate 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

Machine Learning for Cybersecurity
at
University of Chicago
Curriculum

Foundations of Machine Learning for Security

Data-Driven Network and Computer Security

Machine Learning in the Presence of Adversaries

Ethics, Fairness, Responsibility, and Transparency in Data-Driven Cybersecurity

Secure Machine Learning Development & Deployment

Machine Learning for Cybersecurity
at
University of Chicago
Faculty details

Yuxin Chen

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.

Nick Feamster

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.

Blase Ur

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.

Machine Learning for Cybersecurity
at
University of Chicago
Placements

Particulars	Statistics (2025)
% Batch placed	70

View placement details

Machine Learning for Cybersecurity
at
University of Chicago
Entry Requirements

PTE

No specific cutoff mentioned

Other eligibility criteria Up Arrow Icon

Education: Graduate

Conditional Offer Up Arrow Icon

Yes
No programs at the University of Chicago offer conditional admission to students who have not submitted an application following the standard application process and deadlines. A few will make conditional offers of admission to students who otherwise have met the requirements for admission, but do not yet meet the program's English proficiency requirements. If you receive a conditional offer, you will not be allowed to enroll until you have met the requirement; the conditions may not be met after arrival on campus.
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