Transformers in Visual Intelligence

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Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Covers the impact of transformers in computer vision, discussing their architecture, applications, and advancements in various tasks.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

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