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Group Theory: A Primer (2019) Luciano da Fontoura Costa

Tensors in Computations (2021) Lek-Heng Lim

Aspects of Harmonic Analysis and Representation Theory (2021) Gallier & Quaintance

Basic concepts of representation theory (2013) Amritanshu Prasad

Representation Theory of Finite Groups (2012) Bemjamin Steinberg

Group Theory in a Nutshell for Physicists (2016) Anthony Zee

Functions of Matrices: Theory and Computation (2008) Nicholas J. Higham

Convex Optimization (2004) Stephen Boyd, Lieven Vandenberghe

Essence of Group Theory *Beginner-Friendly Mathemaniac

Abstract Algebra Beginner-Friendly Socratica

Euler's formula with introductory group theory Intuition Building 3blue1brown

What is a Tensor? XylyXylyX

Representation Theory Math Doctor Bob

Category Theory for AI Online Course, October 2022

Lie Groups, Lie Algebras, and Representations (2003) Brian C. Hall

Differential Geometry and Lie Groups: A Computational Perspective (2020) Gallier & Quaintance

Introduction to Riemannian Geometry and Geometric Statistics: from basic theory to implementation with Geomstats…

Geometry, Topology and Physics (2003) Nakahara

Differential Forms and Connections (2012) Darling

Connections, Curvature, and Characteristic Classes (2017) Loring W. Tu

Riemannian manifolds, an introduction to curvature (2000) John M. Lee

A Comprehensive Introduction to Differential Geometry (1999) Michael Spivak

Riemannian Geometry (2001) Mikhail Postnikov

Alice’s Adventures in a differentiable wonderland: A primer on designing neural networks (2024) Simone Scardapane

by Simone Scardapane

Introduction to Riemannian Manifolds (2018) John M. Lee

Geometry and Topology

and Symmetry Beginner-Friendly Sean Carroll

Differential Geometry for Computer Science Justin Solomon

Discrete Differential Geometry CMU

What is a Manifold? XylyXylyX

Manifolds Robert Davie

Lie Groups and Lie Algebras XylyXylyX

Lectures on Geometric Anatomy of Theoretical Physics Frederic Schuller

Weekend with Bernie (Riemann) Søren Hauberg @ DTU

Riemann and Gauss Meet Asimov: A Tutorial on Geometric Methods in Robot Learning, Optimization, and Control IROS 2022

Riemannian Geometry and Machine Learning for Non‐Euclidean Data Park and Jang 2019

Introduction to Differential Geometry and Machine Learning Geomstats Jupyter notebooks

Computations and statistics on manifolds with geometric structures.

Differential Geometry for Machine Learning Roger Grosse

Manifolds: A Gentle Introduction Brian Keng

Differential geometry of ML Kyuhyeon Choi

A mathematical framework of intelligence and consciousness based on Riemannian Geometry Meng Lu

The Many Faces of Information Geometry (2022) Frank Nielsen

Parametric information geometry with the package Geomstats (2022) Alice Le Brigant et al.

Nonlinear Dynamics And Chaos: With Applications To Physics, Biology, Chemistry, And Engineering (Studies in…

Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields (1983) John Guckenheimer, Philip Holmes

Introduction to the Modern Theory of Dynamical Systems (2012) Anatole Katok, Boris Hasselblatt

Computational Algebraic Topology Vidit Nanda

Foundations of Topological Data Analysis Robert Ghrist and Vidit Nanda

Topological Data Analysis for Machine Learning Bastian Rieck

Elementary Applied Topology Robert Ghrist

A Survey of Topological Machine Learning Methods (2021) Felix Hensel, Michael Moor, Bastian Rieck

Topological Deep Learning: Graphs, Complexes, Sheaves (2022) Cristian Bodnar

Topological Deep Learning: Going Beyond Graph Data (2023) Mustafa Hajij et al.

Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks (2023) Mathilde…

Algebraic Topology (2001) Allen Hatcher

Allen Hatcher.

Topology (1974) James Munkres

by James R. Munkres

Graph Theory (2008) Adrian Bondy, U.S.R. Murty

Group Invariance Applications in Statistics (1989) Morris Eaton

Group Theoretical Methods in Machine Learning (2008) Risi Kondor, PhD Thesis

Pattern Theory: The Stochastic Analysis of Real-World Signals (2010) David Mumford and Agnès Desolneux

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (2021) _Michael M. Bronstein, Joan Bruna, Taco…

Equivariant Convolutional Networks (2021) Taco Cohen, PhD Thesis

An Introduction to Optimization on Smooth Manifolds (2022) Nicolas Boumal

Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures…

Symmetry in Neural Network Parameter Spaces (2025) Bo Zhao, Robin Walters, Rose Yu

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (2022) Brunton and Katz

Introduction to Smooth Manifolds (2012) John M. Lee

Geometric Deep Learning (2nd Edition) Michael Bronstein, Joan Bruna, Taco Cohen, Petar Veličković @ AMMI

CSC 2547: Current Topics in Machine Learning Methods in 3D and Geometric Deep Learning (2021) Animesh Garg @…

An Introduction to Group-Equivariant Deep Learning (2022) Erik Bekkers @ UvA

Italian Summer School on Geometric Deep Learning (2022) Cristian Bodnar, Michael Bronstein, Francesco Di Giovanni, Pim…

COMP760: Geometry and Generative Models (2022) Joey Bose and Prakash Panangaden @ MILA

Geometric foundations of Deep Learning Michael Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković

What does 2022 hold for Geometric & Graph ML? Michael Bronstein

Geometric Machine Learning for Shape Analysis with Jupyter Notebooks Nina Miolane

Introduction to the Theory of Neural Computation (1991) John Hertz, Anders Krogh, Richard G Palmer

Theoretical Neuroscience (2001) Peter Dayan

Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting (2006) Eugene M. Izhikevich

Neuronal Dynamics: From single neurons to networks and models of cognition (2014) Wulfram Gerstner, Werner M. Kistler,…

Open-access electronic textbook that covers computational and theoretical neuroscience. Provided by École Polytechnique Fédérale de Lausanne (EPFL).

Principles of Neural Design (2015) Peter Sterling & Simon Laughlin

Principles of Neural Science, Sixth Edition (2021) Eric R. Kandel, John D. Koester, Sarah H. Mack, Steven A. Siegelbaum

Rhythms of the Brain (2006) Gyorgy Buzsaki

Networks of the Brain (2010) Olaf Sporns

Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain (2021) Grace…

Generative Models for Neural Data Analysis COSYNE Workshops 2023

Neural Computation VS265 Bruno Olshausen @ UC Berkeley

OpenNeuro

Free and open platform for sharing and analyzing neuroimaging data (MRI, MEG, EEG, iEEG, ECoG, ASL, PET).

NeuroVault

CRCNS

NeuroData Without Borders

Allen Brain Atlas

Kavli Institute for Systems Neuroscience Grid Cell Database

The Natural Scenes Dataset

Geomstats

Computation, statistics, and machine learning on manifolds

Giotto TDA

Topological Data Analysis

E3NN

E(3)-equivariant neural networks

equivariant-MLP

Construct equivariant multilayer perceptrons for arbitrary matrix groups

SHTOOLS

Python library for computations involving spherical harmonics

LieConv

Generalizing convolutional neural networks for equivariance to Lie groups on arbitrary continuous data

Open Neuroscience

A database of open-source tools and software for neuroscience

LieTorch

Geometric machine learning and Lie analysis tools for PyTorch

pyRiemann

Machine learning for multivariate data through the Riemannian geometry of positive definite matrices

Pymanopt

Optimization on manifolds

Geoopt

Riemannian Adaptive Optimization Methods with pytorch optim

TopoX

Computing, embedding, and deep learning on discrete topological domains

TopoBench

Benchmarking topological deep learning

NeurIPS Workshop on Symmetry and Geometry in Neural Representations (2023)

ICML Workshop on Topology, Algebra and Geometry in Machine Learning (2023)

RSS Workshop on Symmetries in Robot Learning (2023)

NeurIPS Workshop on Symmetry and Geometry in Neural Representations (2022)

ICML Workshop on Topology, Algebra and Geometry in Machine Learning (2022)

ICLR Workshop on Geometrical and Topological Representation Learning (2022)

Workshop on Symmetry, Invariance and Neural Representations @ The Bernstein Conference (2022)