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Machine Learning for Cybersecurity 

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  • Estd. 1890

Machine Learning for Cybersecurity
 at 
UChicago 
Overview

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

Duration

5 weeks

Start from

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Total fee

2.50 Lakh

Mode of learning

Online

Official Website

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Course Level

UG Certificate

Machine Learning for Cybersecurity
 at 
UChicago 
Highlights

  • Earn a certificate from the University of Chicago
  • Learn from industry experts
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Machine Learning for Cybersecurity
 at 
UChicago 
Course details

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 
UChicago 
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

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Machine Learning for Cybersecurity
 at 
UChicago 
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 
UChicago 
Entry Requirements

Eligibility criteriaUp Arrow Icon
Conditional OfferUp Arrow Icon
  • Yes

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Machine Learning for Cybersecurity
 at 
UChicago 
Contact Information

Address

Edward H. Levi Hall 5801 South Ellis Avenue Chicago, Illinois 60637 USA
Chicago ( Illinois)

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