Lectures related to Splines and Imaging: From Compressed Sensing to Deep Neural Nets

Explores the historical development and training of multi-layered perceptrons, emphasizing the backpropagation algorithm and feature design.

Covers the motivation and architecture of convolutional networks, from LeNet to AlexNet.

Explores efficient machine learning through data summarization, covering challenges, methods, and impactful applications in various domains.

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.