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Courses

2 available

Introduction 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.

18
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.

16
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. 23 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

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