Resources
The Internet is filled with multiple resources about programming and chemistry, but it is specially difficult to find a repository of curated resources that talk about computational chemistry, specially those related to recent tools in ML and AI. In this page I've tried to gatherd the most useful links, books, tutorials and literature that are relevant for any enthusiast that is willing to take the Journey to Data from a chemical perspective.
Mathematics: *
AI/ML Applications in Chemistry: *
Computational Chemistry: *
Data Science and Statistics: *
https://machinelearningmastery.com
https://www.coursera.org/learn/mindshift-transforma-mente
- Gaussian Process- Regression Playlist: Resources
- Github Jupyter Notebook: https://github.com/MaverickMeerkat/YouTube/blob/master/Stats/Gaussian%20Processes.ipynb
- Gaussian Processes for ML (Rasmussen & Williams): http://www.gaussianprocess.org/gpml/chapters/RW.pdf
- Conditional normal proof: https://stats.stackexchange.com/questions/30588/deriving-the-conditional-distributions-of-a-multivariate-normal-distribution
Reading: A visual introduction to GPs How to chose a kernel: https://www.cs.toronto.edu/~duvenaud/cookbook/
Free Coding Books: https://greenteapress.com/wp/
-->Course (Fast.AI): https://course.fast.ai/#
Cornell Course: Machine Learning for Decision Making, Kilian Weinberger - http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/
Books:
- 100 page ML book
- Introduction to scientific programming with Python (Christin Hill)
- IPython Cookbook https://ipython-books.github.io
Python Resources:
- QuantEcon: https://quantecon.org/lectures/index.html
- Automate The Boring Stuff with Python: https://automatetheboringstuff.com
- Foundations of Applied Mathematics: https://foundations-of-applied-mathematics.github.io
- Practical Business Python: https://pbpython.com
- Hackers and Slackers: https://hackersandslackers.com
The Missing Semester of Your CS Education: https://missing.csail.mit.edu
Machine Learning Basics - Prof. Jan H. Jensen (University of Copenhagen): https://sites.google.com/view/ml-basics/home
DeepLearning.ai -> https://www.deeplearning.ai/programs/
AI Toolkit - NOMAD Novel Materials Discovery): https://nomad-lab.eu/index.php?page=AItutorials
Chemistry Development Kit (CDK) Book: https://egonw.github.io/cdkbook/
Macs in Chemistry: https://www.macinchem.org
Liu Lab Computational Chemistry Tutorials: https://liu-group.github.io
MoleculeNet, a benchmark for molecular machine learning: http://moleculenet.ai
DeepChem: https://deepchem.io
Computational and Inferential Thinking: The Foundations of Data Science: https://inferentialthinking.com/chapters/intro.html
Data 8: The Foundations of Data Science: http://data8.org
Neural Networks from Scratch in Python: https://nnfs.io
Neural Networs and Deep Learning (book with project): http://neuralnetworksanddeeplearning.com/index.html
How to learn ML in 6 Months
- MIT Linear Algebra Open Course
- MIT Calculus Open Course
- MIT Stats and Probability Course
Roadmap for Thesis:
- Hands-on con Data Professor (Solubility & other projects)
- Curso Pythoninchemistry (https://pythoninchemistry.org)
- Curso Prof. Ayers (https://qchem1.qcdevs.org)
- Curso Coursera John W. Daily (https://www.coursera.org/learn/quantum-mechanics)
- Curso Machine Learning - TUM
- Curso GitHub
- Curso rápido introductorio Python (EdX)
- Curso Computational Chemistry TMP Chemistry
- Cursos QC-Edu
Hot Topics:
- Molecular representation
- Relevant features for ML in chemical applications
- Reactivity prediction
- Other useful things