In this lecture, I will give an introduction to the field of continuous optimization. I will emphasize instances of optimization problems that appear in biology and physics through the concept of optimization landscapes. I will review sampling-based approaches as well as gradient-based methods and focus on concepts rather than derivations of specific algorithms. The lecture is intended to set...

A 1-hour version of the MLSS Buenos Aires tutorial notes, focusing on parts 1 and 4, available at the links below.

We overview various numerical methods to solve ODEs and PDEs.

For source of notes see: https://github.com/ahbarnett/fwam-numpde

An introduction to Bayesian hierarchical modeling, with an example from my own research modeling repeated velocity measurements of distant stars in the Milky Way.

A Deep Learning 101 to get familiar with machine/deep learning principles, neural networks, back-propagation, convolution nets and representation learning.

We will introduce state of the art deep learning methods and showcase some of its applications to astrophysical challenges.

The goal of this talk is to show how probabilistic methods can be used to accelerate standard matrix factorizations (e.g. SVD) with provable characteristics in terms of speed and accuracy.

I will focus on clustering data points in low dimensions (mostly 2d) and provide an overview of some popular clustering algorithms.

The accompanying live notebook is linked from my homepage: https://users.flatironinstitute.org/~magland

Tensor network methods are a family of variational algorithms used to simulate many body quantum systems in a variety of situations. With some brief motivation from physics, I'll explain why anyone would want to use these methods, why it is that they are so effective for certain classes of problems, and some extensions to other fields like machine learning.