Lecture
This lecture focuses on perception in autonomous vehicles, covering topics such as image classification challenges, machine learning concepts, linear regression, nearest neighbor approach, hyperparameters setting, and loss functions. The instructor explains the difficulties in classifying images with changing viewpoints, illumination, deformation, and occlusion. They introduce machine learning as a method for systems to improve performance through experience. The lecture also delves into the concept of generalization and the importance of controlling model capacity. Additionally, the instructor discusses linear classifiers, distance metrics for comparing images, and the k-nearest neighbors algorithm. The lecture concludes with an overview of optimization techniques and representation learning for finding the best model parameters.