Online Textbooks

Below is a collection of free / open online textbooks at the interface between chemistry, materials and machine learning / AI.

Foundations

• Introduction to Scientific ML
  Intermediate

  Online lecture-book on scientific machine learning with interactive coding tutorials for data analytics and uncertainty quantification.

  Prior knowledge: Python, basic calculus, linear algebra
  Estimated time: TODO

  scientific machine learningtextbooknotebooks

• Bayesian Modeling and Computation in Python
  Intermediate

  Applied textbook that walks through Bayesian computation using PyMC, ArviZ and TensorFlow Probability. Includes practical code-examples and notebooks. Tutorials included.

  Prior knowledge: Probability theory, linear algebra, basic Python or R
  Estimated time: 20–40 hours

  bayesian statisticsprobabilistic programmingpython notebooks

• Dive into Deep Learning
  Beginner

  Interactive textbook with Jupyter notebooks and runnable code (PyTorch, MXNet) for deep learning. Great hands-on start.

  Prior knowledge: Basic Python programming, linear algebra
  Estimated time: 30–60 hours

  deep learningnotebookspython

• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
  Advanced

  Advanced textbook covering geometric deep learning topics (manifolds, graphs, gauge theory) with some code examples. No heavy notebooks listed.

  Prior knowledge: Deep learning, differential geometry, graph theory
  Estimated time: 20–40 hours

  geometric deep learninggraph neural networksmanifolds

• Interpretable Machine Learning
  Intermediate

  Book for practitioners wanting to understand model interpretability. Includes Python notebook examples for many methods.

  Prior knowledge: Machine learning basics, Python
  Estimated time: 20–30 hours

  interpretable MLmachine learningpython

• Understanding Deep Learning
  Intermediate

  Textbook providing up-to-date deep learning topics (transformers, diffusion models) with exercises and interactive slides; some notebooks included.

  Prior knowledge: Basic deep learning concepts, Python
  Estimated time: 15–30 hours

  deep learningtransformersvisual explanations

• An Introduction to Statistical Learning
  Intermediate

  Accessible textbook on statistical learning, covering regression, classification, resampling methods, regularization, tree-based methods, SVMs, clustering, survival analysis, and more. Free PDF versions and video lectures are available, with R and Python labs at the end of each chapter for hands-on practice.

  Prior knowledge: Basic probability and statistics, linear algebra, and some R or Python experience
  Estimated time: 30–60 hours (full book with labs)

  statistical learningregression & classificationR/Python labs

Chemistry

• Scientific Computing for Chemists with Python
  Beginner To Intermediate

  Online textbook on programming and scientific computing for chemists, featuring Python-based coding tutorials.

  Prior knowledge: None, starts from basics of Python programming
  Estimated time: 10 - 15 hours (basics) + 15-30 hours (advanced topics)

  pythonscientific computingchemistrytextbook

• Deep Learning for Molecules and Materials
  Beginner To Intermediate

  Textbook focused on deep learning approaches for molecules and materials. Contains Jupyter-book style chapters and notebook examples for hands-on learning.

  Prior knowledge: Chemistry fundamentals, Python, basic ML
  Estimated time: 15–30 hours

  moleculesdeep learningmaterials informatics

Materials