In the course "Securing AI and Advanced Topics", learners will delve into the cutting-edge intersection of AI and cybersecurity, focusing on how advanced techniques can secure AI systems against emerging threats. Through a structured approach, you will explore practical applications, including fraud prevention using cloud AI solutions and the intricacies of Generative Adversarial Networks (GANs). Each module builds upon the previous one, enabling a comprehensive understanding of both offensive and defensive strategies in cybersecurity.
Securing AI and Advanced Topics
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Securing AI and Advanced Topics
This course is part of AI for Cybersecurity Specialization
Instructor: Lanier Watkins
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
15 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Learn to implement AI-based solutions to detect and prevent credit card fraud in cloud environments.
Explore the fundamentals of Generative Adversarial Networks and their applications in generating synthetic data.
Gain hands-on experience with black-box and white-box adversarial attacks to assess and enhance model resilience.
Master techniques in feature engineering and performance evaluation to optimize AI models for cybersecurity applications.
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
September 2024
Assessments
15 assignments
Taught in English
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This course is part of the AI for Cybersecurity Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course
This course provides a comprehensive exploration of AI-based solutions for credit card fraud detection, emphasizing the implementation and evaluation of advanced algorithms, including Generative Adversarial Networks (GANs). Students will gain practical experience in executing adversarial attacks and optimizing machine learning models, enhancing their ability to develop robust AI systems. Through hands-on projects, participants will synthesize knowledge to address real-world challenges in fraud detection and model resilience.
What's included
2 readings
In this module, we study the background of threats that prevent credit card fraud. Then, we investigate hands-on credit card fraud detection implementations. Also, we discuss metrics to evaluate the performance of credit card fraud detection algorithms.
What's included
2 videos3 readings3 assignments
In this module, we study generative adversarial networks (GANs) background. Then, we investigate a hands-on GAN implementation and how it can be used to develop synthetic data likely indistinguishable from the real data.
What's included
2 videos3 readings3 assignments
In this module, we will discuss black and white-box adversarial attacks. Also, we will explore hands-on implementations of several adversarial attacks.
What's included
2 videos3 readings3 assignments1 ungraded lab
In this module we will study reinforcement learning (RL) and how it can be used for adversarial attacks. Also, we will study data engineering techniques to optimize datasets to help improve ML model performance.
What's included
2 videos3 readings3 assignments
In this module, we will discuss feature engineering and model optimization techniques. Also, we will explore ML model performance metrics.
What's included
2 videos3 readings3 assignments
Instructor
Offered by
Recommended if you're interested in Security
Johns Hopkins University
Board Infinity
Amazon Web Services
Johns Hopkins University
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