Machine Learning for Quantum Matter - Lecture 1

Explore machine learning applications in quantum matter through this comprehensive lecture, the first in a four-part series. Delve into classical algorithms, high-dimensional data analysis, and low-dimensionality concepts. Examine the fundamentals of machine learning, including natural language processing and its societal implications. Investigate various machine learning categories, focusing on data-driven learning, physical laws, and supervised learning techniques. Learn about neural networks, activation functions, loss functions, and gradient descent. Apply these concepts to a square lattice toy model, gaining practical insights into machine learning's role in quantum physics research.

Syllabus

Introduction
Classical Algorithms
Machine Learning
High Dimensionality
Low Dimensionality
Learning
Examples
Natural Language Processing
Chart GPT
Educational societal implications
Example poem
Example Alphafall
Machine Learning Categories
Data Driven Learning
Equations and Physical Laws
Supervised Learning
Classification
Neural Networks
Activation Functions
Neural Network
Loss Function
Small W
Gradient Descent
Learning Rate
Square Lattice
Toy Model