本课程首先介绍了机器学习、安全、隐私、对抗性机器学习和博弈论等主题。然后从研究的角度，讨论各个课题和相关工作的新颖性和潜在的拓展性。通过一系列的阅读和项目，学生将了解不同的机器学习算法，并分析它们的实现和安全漏洞，并培养开展相关主题的研究项目的能力。

https://aisecure.github.io/TEACHING/2020_fall.html

• Evasion Attacks Against Machine Learning Models (Against Classifiers)

• Evasion Attacks Against Machine Learning Models (Non-traditional Attacks)

• Evasion Attacks Against Machine Learning Models (Against Detectors/Generative Models/RL)

• Evasion Attacks Against Machine Learning Models (Blackbox Attacks)

• Detection Against Adversarial Attacks

• Defenses Against Adversarial Attacks (Empirical)

• Defenses Against Adversarial Attacks (Theoretic)

• Poisoning Attacks Against Machine Learning Models
  

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