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CTU-AI410.AU1
Adversarial Learning
- Practice in 21 Hands-On Labs — nothing to install
- 5 Interactive Lessons and 33 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
21 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
5Interactive Lessons
33Topics
21LiveLab
6Videos
66Flashcards
66Glossary of terms
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
5 Interactive Lessons · 33 topics01 Fundamentals of Adversarial Learning 7 topics · 6 LiveLab +
- Adversarial Learning Frameworks
- Adversarial Security Mechanisms
- Stochastic Game Illustration in Adversarial Deep Learning
- Learning Curve Analysis for Supervised Machine Learning
- Adversarial Loss Functions for Discriminative Learning
- Adversarial Examples in Deep Networks
- Adversarial Examples for Misleading Classifiers
6 LiveLab in this lesson — see the labs panel →
02 Applying Adversarial Techniques 3 topics · 3 LiveLab +
- Generative Adversarial Networks
- Generative Adversarial Networks for Adversarial Learning
- Transfer Learning for Domain Adaptation
3 LiveLab in this lesson — see the labs panel →
03 Defense Strategies Against Adversarial Attacks 15 topics · 5 LiveLab +
- Security and Privacy in Adversarial Learning
- Feature Weighting Attacks
- Poisoning Support Vector Machines
- Robust Classifier Ensembles
- Robust Clustering Models
- Robust Feature Selection Models
- Robust Anomaly Detection Models
- Robust Task Relationship Models
- Robust Regression Models
- Adversarial Machine Learning in Cybersecurity
- Securing Classifiers Against Feature Attacks
- Adversarial Classification Tasks with Regularizers
- Adversarial Reinforcement Learning
- Computational Optimization Algorithmics for Game Theoretical Adversarial Learning
- Defense Mechanisms in Adversarial Machine Learning
5 LiveLab in this lesson — see the labs panel →
04 Ethical Implications of Adversarial Learning 5 topics · 4 LiveLab +
- Game Theoretical Learning Models
- Game Theoretical Adversarial Learning
- Game Theoretical Adversarial Deep Learning
- Stochastic Games in Predictive Modeling
- Robust Game Theory in Adversarial Learning Games
4 LiveLab in this lesson — see the labs panel →
05 Applying Adversarial Techniques - Advanced Topics 3 topics · 4 LiveLab +
- Adversarial Attacks on Images
- Adversarial Attacks on Texts
- Spam Filtering
4 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
21 LiveLabs- Exploring the Adversarial Learning Framework
- Comparing Classifier Robustness Against Adversarial Attacks
- Evaluating Classifier Performance Under Gaussian Noise
- Simulating Stochastic Defender-Attacker Decisions
- Understanding Adversarial Examples
- Fooling a Neural Network with Tiny Perturbations
- Building and Training a Simple GAN
- Understanding a Black-Box Attack
- Evaluating Transfer Learning Across Different Data Domains
- Performing a Simple Dataset Poisoning Attack
- Analyzing Classifier Behavior Under Input Perturbations
- Modeling Learner-Versus-Adversary Interactions
- Exploring Adversarial Attack Surfaces
- Understanding Adversarial Defense Mechanisms
- Identifying Suspicious Inputs Using Prediction Confidence
- Analyzing Game-Theoretical Adversarial Interaction
- Protecting an IDS Against Adversarial Inputs
- Protecting an IDS Against Adversarial Inputs
- Generating Adversarial Images to Mislead Classifiers
- Exploring Character-Level Perturbations in Text Classification
- Understanding Spam Filtering
- Evading and Strengthening Spam Filters Against Adversarial Messages
Labs run in your browser — nothing to install.
02 / FAQs
Questions before you start
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- 1 year of full access
- 21 LiveLab included
- Certificate of completion
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