When: Fridays 11am-11:50am
Justin Terry, organized by Dan Lathrop
Fourth lecture: Theory of Neural Networks (history, motives, limitations, algorithms, transforms)
Course description: This short course covers the fundamentals of machine learning, assuming the typical background and motives of a physicist. It's goal is to give those who attend an understanding of the field and its capabilities, as well the tools to learn the necessary extensions of the topic to apply it to their physics research. Lectures will include introductions to Python, Linux, neural nets, deep learning, natural language processing, imagine recognition/computer vision, and AI safety.