This lecture covers the basics of machine learning, including supervised and unsupervised learning, model evaluation, and feature selection. It also delves into advanced topics such as neural networks and deep learning.
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Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.
