Update: what machine learning resources are up to date for end of 2025?

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Broad links:

https://www.aisafety.com/map

https://www.aisafety.com/self-studyhttps://www.ml4good.org/

Courses:

Andrew Ng’s Machine Learning specialization: https://www.deeplearning.ai/courses/machine-learning-specialization/

Practical Deep Learning from Fast AI: https://course.fast.ai/

MIT Intro to Machine Learning: https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/course/

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Intro to ML Safety is run by the Center for AI Safety and designed and taught by Dan Hendrycks, a UC Berkeley ML PhD and director of the Center for AI Safety.  Last run in 2023, this is an 8-week virtual course that aims to introduce students with a deep learning background to the latest empirical AI Safety research (publicly available at course.mlsafety.org)

Long list of resources courtesy of GabeM (Sept 2022)

https://forum.effectivealtruism.org/posts/S7dhJR5TDwPb5jypG/levelling-up-in-ai-safety-research-engineering

Level 3: Machine Learning

Objective‏‏‎ ‎

You have the mathematical context necessary for understanding Machine Learning (ML). You know the differences between supervised and unsupervised learning and betweenclassification and regression. You understand common models linear regression, logistic regression, neural networks, decision trees, and clustering, and you can code some of them in a library like PyTorch or JAX. You grasp core ML concepts like loss functions, regularization, bias/variance, optimizers, metrics, and error analysis.

Why so much math? Machine learning at its core is basically applied statistics and multivariable calculus. It used to be that you needed to know this kind of math really well, but now with techniques like automatic differentiation,you can train neural networks without knowing much of what’s happening under the hood. These foundational resources are included for completeness, but you can probably spend a lot less time on math (e.g.the first few sections of each course) depending on what kind of engineering work you intend to do. You might want to come back and improve you math skills for understanding certain work in Levels 6-7,though, and if you find this math really interesting, you might be a good fit for theoretical AI alignment research.

Goals‏‏‎ ‎

• Understand the mathematical basis of Machine Learning, especially linear algebra and multivariable calculus.
• Write out the differences between supervised and unsupervised learning and between classification and regression.
• Train and evaluate a simple neural network on a standard classification task like MNIST or a standard regression task like a Housing Dataset.

Resources‏‏‎ ‎

A. Introductory Machine Learning (Choose 1-2)

1. Course 6.036 - MIT Open Learning Library
2. Machine Learning by Stanford University - Coursera
3. Introduction to Machine Learning - Udacity
4. Introduction to Machine Learning - Google
5. Advanced Introduction to Machine Learning - CMU
6. Supervised Machine Learning: Regression and Classification- DeepLearning.AI

B. Additional Resources

1. pytorch_exercises - Kyubyong
2. Writing better code with pytorch+einops - einops
3. Contemporary ML for Physicists - Jared Kaplan

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Level 4: Deep Learning

Objective‏‏‎ ‎

You’ve dived deeper into Deep Learning(DL) through the lens of at least one subfield such as Natural LanguageProcessing (NLP), Computer Vision (CV), or Reinforcement Learning (RL).You now have a better understanding of ML fundamentals, and you’ve reimplemented some core ML algorithms “from scratch.” You’ve started to build a portfolio of DL projects you can show others.

Goals‏‏‎ ‎

• Be able to describe in moderate detail a wide range of modern deeplearning architectures, techniques, and applications such as longshort-term memory networks (LSTM) or convolutional neural networks(CNN).
• Gain a more advanced understanding of machine learning by implementing autograd, backpropagation, and stochastic gradient descent “from scratch.”
• Complete 1-3 deep learning projects, taking 10–20 hours each, in 1 or more sub-fields like NLP, CV, or RL.

Resources‏‏‎ ‎

1. General Deep Learning (Choose 1)
   1. Practical Deep Learning for Coders - fast.ai
   2. Deep Learning by deeplearning.ai - Coursera
   3. Deep Learning - NYU Spring 2021
   4. PyTorch Tutorials
   5. Deep Learning for Computer Vision - UMich (Lectures 1-13 only)
   6. Deep Learning Online Training Course - Udacity
   7. Neural networks and deep learning - Michael Nielsen
2. Advanced Machine Learning
   1. Studying (Choose 1-2)
      1. Backpropagation - CS231n Convolutional Neural Networks for Visual Recognition
      2. A Recipe for Training Neural Networks - Andrej Karpathy
   2. Implementing (Choose 1)
      1. MiniTorch (reimplement the core of PyTorch, self-study tips here)
      2. building micrograd - Andrej Karpathy
      3. Autodidax: JAX core from scratch

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