Useful Resources
📚 Core Learning Materials
Course Books
- Murphy, K. P. (2022). Probabilistic machine learning: an introduction. Free online
- Brunton, S. L., & Kutz, J. N. (2022). Data-driven science and engineering. Free online
- Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning Free online version.
- Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. “O’Reilly Media, Inc.”
- Kutz, J. N. (2013). Data-driven modeling & scientific computation: methods for complex systems & big data. Oxford University Press.
Online Courses
- Main Machine Learning Reference: Stanford’s Introduction to Machine Learning CS229
- Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications
- MIT ML Courses
- FastAI
- Modern applications of machine learning in quantum sciences
🧪 Scientific ML Resources
Papers
General AI for Science Papers and Articles
- Is AI leading to a reproducibility crisis in science?
- Artificial intelligence and illusions of understanding in scientific research
- Scientific discovery in the age of artificial intelligence
Scientific Machine Learning Papers
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Distilling free-form natural laws from experimental data
- Physics-informed neural networks
- Physics-informed machine learning
- Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
- Deep learning for universal linear embeddings of nonlinear dynamics
- Learning to Simulate Complex Physics with Graph Networks
- Modern Koopman Theory for Dynamical Systems
- Accelerating Eulerian Fluid Simulation With Convolutional Networks
- Deep learning and process understanding for data-driven Earth system science
- Accelerating Eulerian Fluid Simulation With Convolutional Networks
- Historical book: Rediscovering Chemistry with the Bacon System
Talks & Lectures
- Science in the Age of Experience 2024 - George Karniadakis, Brown University
- Scientific Machine Learning Seminar compilation
- Physics informed ML Seminar Series - University of Washington
- PINNs: Physics Informed Neural Networks - Ben Moseley
- State of SciML Scientific Machine Learning - Chris Rackauckas
- Nathan Kutz - The Dynamic Mode Decomposition - A Data-Driven Algorithm
🔧 Programming Tools
ML & Deep Learning
Data Science
📊 Data Sources
- UCI Machine Learning Repository
- Kaggle
- Google Dataset Search
- US weather data
- US NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION data
- NASA data
🎥 Media & News
YouTube Channels
News & Articles
- Toward a real-time decoding of images from brain activity
- Science Is Becoming Less Human
- Author Admits She Used ChatGPT to Write Award-Winning Novel
- How Much of the World Is It Possible to Model?
🎯 Topic-Specific Resources
Deep Learning
- MIT Introduction to Deep Learning - 2023
- Introduction to Neural networks (series) by 3Blue1Brown
- Introduction to Graph Neural Networks (series)
- Free Code Camp Introduction (2019)
- Andrei Karpathy’s hands-on intro
- Watching Neural Networks Learn
- LabML: papers with Code
- Introduction to Diffusion Models
- Introduction to Recurrent Neural Networks by StatQuest
- Introduction to Graph Neural Networks - Microsoft
- Train Deep Learning to Identify Doodles - S. Lague
Deep Learning for Time-series Analysis
Machine Learning for Scientific Computing
- ETH Zurich Deep Learning in Scientific Computing (series) (2023)
- How Well Can DeepMind’s AI Learn Physics? (2021)
- Introduction to Physics Informed Neural Networks
- Physics informed machine learning - Steven Brunton
- Machine Learning for Fluid Dynamics - Steven Brunton
Deep Learning for Large Language Models
- Word Embedding by StatQuest
- Introduction to Large Language Models by Andrei Karpathy
- Geoffrey Hinton’s talk on Intelligence and LLMs (2024)
- ChatGPT: 30 Year History - How AI Learned to Talk
- But what is a GPT? Visual intro to transformers - 3Blue1Brown
- Transformer Neural Networks, ChatGPT’s foundation, Clearly Explained - StatQuest
- The Transformer, Attention is All You Need paper review - Yannic Kilcher