Learning
- How I learn machine learning | ★❤✰ Vicki Boykis ★❤✰; 2022.
- Machine Learning Engineer Roadmap | /home/vigi99
- ludwigbap/ml learning resources
- Stanford CS229: Machine Learning, Spring 2022 Notes)
- Lecture Videos Playlist on Youtube Learning from Data from Caltech; Youtube, also uses the eponymous textbook
When people are getting ready to learn machine learning at MSc level, they don’t learn from a course that says “if you don’t understand it it doesn’t matter”, they learn from this course instead. Abu-Mostafa’s Caltech course is probably the best first course that delivers good understanding of machine learning. via
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Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.
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Machine Learning for MacroEconomics by Jesús Fernández-Villaverde | University of Pennsylvania
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Home | CS 229S; Systems for Machine Learning
Resources
- franknielsen / Lists of curated books
- stas00/ml-engineering: Machine Learning Engineering Guides and Tools
- MAD - Machine learning, Artificial intelligence and Data; ( PDF chart of the landscape); related reading - The 2023 MAD (Machine Learning, Artificial Intelligence & Data) Landscape – Matt Turck; The interactive 2024 version
Video
- hu-po - YouTube; Livestreams on ML papers, Coding, Research.
Blogs
- Eugene Yan
- ApplyingML - Papers, Guides, and Interviews with ML practitioners by Eugene Yan
- Chip Huyen
Papers
Browse the State-of-the-Art in Machine Learning | Papers With Code
Pen and Paper Exercises in Machine Learning
This is a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimisation, directed graphical models, undirected graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning (including ICA and unnormalised models), sampling and Monte-Carlo integration, and variational inference.
See also: machine-learning-books