Courses
2 availableIntroduction to Machine Learning
From Data to Models
The foundations of machine learning — regression, classification, neural networks, and unsupervised learning — from first examples to working models.
8
Lectures
ML for Science
Data-Driven Modeling in Science & Engineering
Interactive lectures covering the foundations of scientific modeling, numerical methods, dynamical systems, and machine learning — from first principles to physics-informed neural networks.
15
Lectures
Machine Learning: from basics to scientific modeling
What leads to what. Start at the top; each row assumes the rows above it. Hover a lecture to see what it needs and what it leads to. 20 of 25 lectures are open so far.
Intro to ML
ML for Science
Foundations
Not published yet
Linear Algebra
Vectors, matrices, eigenvalues
Programming
Python, loops, functions, arrays
Calculus
Derivatives, the chain rule, and what a gradient is
Introduction and Logistics
Intro to ML
Differential Equations
How a rate of change becomes a trajectory
A Brief History of Empirical Modeling
soon
Probability and Statistics
Distributions, expectation, likelihood
What is a Model?
soon
Math and Python: Your Computational Toolkit
Intro to ML
Learning, Examples, and Data
Intro to ML
Scientific Modeling Principles and Differential Equations
ML for Science
SVD & PCA: Dimensionality Reduction
ML for Science
Supervised Learning & Linear Regression
soon
Numerical Computing and Differential Equations
ML for Science
Linear Dynamical Systems & DMD
ML for Science
Classification & Logistic Regression
soon
Feature Engineering and Generalization
Intro to ML
Complex Systems and Probabilistic Modeling
ML for Science
MLE and Generalized Linear Models
Intro to ML
Introduction to Neural Networks
soon
Time Series Analysis: Starting from Data
ML for Science
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