Lectures

Introduction to Data-Driven Modeling with Applications
tl;dr: A brief history of artificial intelligence and the latest breakthroughs.
[slides]

Supervised Learning and Linear Regression
tl;dr: Empirical laws, linear models, and the least squares method.
[Notes] [Code] [Notebook] [Video]

Project proposal instructions
tl;dr: Guidelines for the final project proposal.
[Notes]

Nonlinear Systems and Uncertainty Quantificaiton
tl;dr: A brief introduction to Lorenz systems and motivating the need for probabilistic modeling.
[Notes]

Sparse Identification of Differential Equations
tl;dr: A brief introduction to discovering nonlinear differential equations from data with sparse regression techniques.
[Notes] [PDE/Symbolic Reg]

Principle Component Analysis
tl;dr: An introduction to unsupervised learning through the lens of dimensionality reduction, PCA and SVD.
[Notes]

Introduction and Overview of Neural Networks
tl;dr: An overview of basic concepts, advanced topics and applications of deep learning.
[slides] [Code] [Notebook]