Threat Modeling: Adversarial Examples and Backdoors

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Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Explores modern approaches and challenges in acquiring data for learning optimal controllers through demonstrations and data-driven methods.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

Discusses Generative Adversarial Networks and their applications in synthesizing data and generating images.

Covers fundamental principles, opportunities, and challenges in machine learning.

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.