Machine Learning, Deep Learning & Artificial Intelligence in Chemistry: A Bunch of Resources

Books & Websites

• deep learning for molecules and materials - Andrew White WEBSITE
• Pattern Recognition & Machine Learning - Christopher Bishop BOOK
• Undestanding Molecular Simulation: From Algorithms to Applications - Daan Frenkel & Berend Smit BOOK
• Essentials of Computational Chemistry: Theories and Models - Christopher Cramer BOOK
• Molecular Modelling: Principles and Applications - Andrew Leach BOOK
• Understanding Molecular Simulation: From Algorithms to Applications - Daan Frenkel and Berend Smit BOOK

Talks

• Molecular Representations for Drug Discovery - Djork-Arné Clevert YOUTUBE
• Representation and generation of molecular graphs - Tommi Jaakkola YOUTUBE
• Advancing molecular simulation with deep learning - Frank Noe YOUTUBE
• Uncertainty-aware machine learning models of many-body atomic interactions - Boris Kozinsky YOUTUBE
• Machine learning potentials: from polynomials to message passing networks - Gabor Csányi YOUTUBE
• Diffusion based distributional modeling of conformers, blind docking and proteins - Tommi Jaakkola YOUTUBE
• Modeling with Machine Learning: Challenges and Some Solutions - Tommi Jaakkola YOUTUBE
• Language is the future of chemistry - Andrew White YOUTUBE
• Deep Learning the Next Twenty Years of Metamaterials - Willie Padilla YOUTUBE
• LLMs and GPT4 in Materials and Chemistry (How to Be a Chemist in 2023) - Andrew White YOUTUBE
• Iterative Molecular Discovery with interpretable Deep Learning - Andrew White YOUTUBE
• The state of neural network interatomic potentials - Justin Smith YOUTUBE
• Why ML Can Find a New Material, But Not a Needle in a Haystack - Kevin Jablonka YOUTUBE
• Machine learning for atomic-scale modeling: potentials and beyond - Michele Ceriotti YOUTUBE
• Gaussian Process Approximation & Uncertainty Quantification for Autonomous Experiment - Marcus Noack YOUTUBE
• The Pitfalls of Using ML-based Optimization - Tina Eliassi-Rad YOUTUBE

Starting from scratch?

Intro to Computer Science Courses

• Harvard CS50: Introduction to Computer Science YOUTUBE
• MIT 6.0001 Introduction to Computer Science and Programming in Python YOUTUBE
• MIT 6.0002 Introduction to Computational Thinking and Data Science YOUTUBE

Intro to Machine Learning, Deep Learning & Artificial Intelligence

• MIT 6.034 Artificial Intelligence YOUTUBE
• StatQuest with Josh Starmer YOUTUBE
• Kaggle WEBSITE
• The Hundred Page Machine Learning Book - Andriy Burkov WEBSITE
• Deep Learning- Ian Goodfellow, Yoshua Bengio and Aaron Courville WEBSITE
• Dive into Deep Learning WEBSITE
• fast.ai's Practical Deep Learning for Coders - Jeremy Howard WEBSITE
• Introduction to Machine Learning: Draft of Incomplete Notes - Nils J. Nilsson BOOK
• Machine Learning with PyTorch and Scikit-Learn - Sebastien Raschka, Yuxi Liu and Vahid Mirjalili
• Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
• Deep Learning with Python - François Chollet
• Machine Learning for Absolute Beginners - Oliver Theobald
• NYU Deep Learning YOUTUBE

Computer vision

• Computer Vision: Algorithms and Applications - Richard Szeliski BOOK
• The Anciest Secrets of Computer Vision - Joseph Redmon YOUTUBE
• Deep Learning for Vision Systems - Mohamed Elgendy

For fun

• Algorithms for Decision Making - Mykel Kochenderfer, Tim Wheeler and Kyle Wray WEBSITE
• MIT 6.042J Discrete Mathematics/Mathematics for Computer Science YOUTUBE
• MIT 18.404J Theory of Computation YOUTUBE
• MIT 6.868J The Society of Mind YOUTUBE
• MIT 6.001 Structure and Interpretation, 1986 YOUTUBE
• SICP Reading Group YOUTUBE
• Packaging Scientific Software with Conda-Forge - Jan Janssen YOUTUBE
• Computing at the Moiré Scale - Mitchell Luskin YOUTUBE
• Legacy of Computers / The role of programming - Gerald Jay Sussman LONG | SHORT

All links are from public sources.