Lectures related to Adversarial Machine Learning: Theory and Applications

Explore the vulnerabilities of neural networks to adversarial attacks and the strategies to enhance model robustness.

Covers the fundamentals of multilayer neural networks and deep learning.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Explores adversarial examples, defenses, and certifiable robustness in deep learning, including Gaussian smoothing and perceptual attacks.

Explores document analysis, topic modeling, and generative models for data generation in machine learning.