Useful Resources
Course book references
- Murphy, K. P. (2022). Probabilistic machine learning: an introduction. MIT press. Free online version
- Brunton, S. L., & Kutz, J. N. (2022). Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press. Free online version
- 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.
Other online references
- Stanford’s Introduction to Machine Learning CS229
- Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications
- Modern applications of machine learning in quantum sciences
Data sources
- UCI Machine Learning Repository
- Kaggle
- Google Dataset Search
- US weather data
- US NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION data
- NASA data
Recommended papers
- Scientific discovery in the age of artificial intelligence
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Deep learning for universal linear embeddings of nonlinear dynamics
- Learning to Simulate Complex Physics with Graph Networks
- Modern Koopman Theory for Dynamical Systems
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Distilling free-form natural laws from experimental data
- 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
YouTube Channels
- My channel has a few useful videos: Joseph Bakarji
- Steven Brunton’s YouTube channel on data-driven fluid dynamics and control
- Two Minute Papers
- StatQuest
Useful articles
In the news
- 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?
Other Videos
- AI Learns to Walk (deep reinforcement learning)
- Watching Neural Nets Learn
- How Well Can DeepMind’s AI Learn Physics?
- Will AI discover new physics, Lisa Randall and Lex Fridman
- How “Digital Twins” Could Help Us Predict the Future - Karen Willcox - TED
Talks
- 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
Related Courses:
- Scientific Machine learning
- General Machine Learning
- MIT ML Courses
- FastAI
- Stanford Intro to ML - CS229: YouTube, Course website
- Stanford Intro to AI - CS221: YouTube. Course website
- Harvard’s CS50
- Deep Learning Specialization - Andrew Ng
- Neural networks: zero to hero
- Andrej Karpathy
- Chris Olah’s blog
Coding Resources
Useful Python Packages
Python for ML and deep learning
Python for data science
Python Tutorials
References Per Topic
Dimensionality Reduction and Modal Decomposition
- Understanding POD: the Proper Orthogonal Decomposition
- Dynamic Mode Decomposition (Overview) - Steven Brunton
Reinforcement Learning
- AI Learns to Walk (deep reinforcement learning)
- Reinforcement Learning: An Introduction by Sutton and Barto
- Intro to Reinforcement Learning by Steven Brunton
Deep Learning
Introductions
- 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](https://www.youtube.com/watch?v=OFS90-FX6pg&ab_channel=ArtoftheProblem) - 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
Variational Autoencoders
Graph Neural Networks
Modeling and Simulation
Differential Equations
- Introduction to Differential Equations by 3Blue1Brown
- Introduction to Differential Equations by MIT
- Engineering Math: Differential Equations and Dynamical Systems - Steven Brunton (2024)