Archive

• Patterns, Predictions, and Actions by Moritz Hardt and Benjamin Recht; PDF, problem sets.
• Learning Theory from First Principles by Francis Bach.
• Foundations of Machine Learning by Mehryar Mohri.
• Understanding Machine Learning: From Theory to Algorithms by  Shai Shalev-Shwartz and Shai Ben-David

Heard it first time via Valeriy Manokhin on X. His feed often posts on well regarded mathematical learning materials on archive.org.

Introduction

• How statisticians got prediction wrong by Manokhin on X.
• Predicting with confidence: the best machine learning idea you never heard of by Scott Locklin, 2016.

Tutorials

• [2107.07511] A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
• Conformal Prediction by Ryan Tibshirani.
• A Tutorial on Conformal Prediction Journal of Machine Learning Research 9 (2008), by Glenn Shafer and Vladimir Vovk

Papers

• (Lei et al. 2017)

Links

• valeman/awesome-conformal-prediction: A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

References

• Lei, G'Sell, Rinaldo, Tibshirani, Wasserman (2017). Distribution-Free Predictive Inference for Regression.

Principal Component Analysis is mostly commonly used for dimensionality reduction.

It is a data preperation technique performed on data prior to modeling. After data cleaning and data scaling and before training a predictive model.

A tutorial on Principal Components Analysis -- a technical report with code in scilab.

Implementing a Principal Component Analysis (PCA) in Python, step by step by Sebastian Raschka, who has written Machine learning and AI beyond the basics

MLOps Requirements

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visenger/awesome-mlops: A curated list of references for MLOps

MLOps References

Papers

Operationalizing machine learning: An interview study (Shankar et al. 2022)

The process of operationalizing ML, or MLOps, consists of a continual loop of (i) data collection and labeling, (ii) experimentation to improve ML performance, (iii) evaluation throughout a multi-staged deployment process, and (iv) monitoring of performance drops in production. When considered together, these responsibilities seem staggering -- how does anyone do MLOps, what are the unaddressed challenges, and what are the implications for tool builders? We conducted semi-structured ethnographic interviews with 18 MLEs working across many applications, including chatbots, autonomous vehicles, and finance. Our interviews expose three variables that govern success for a production ML deployment: Velocity, Validation, and Versioning. We summarize common practices for successful ML experimentation, deployment, and sustaining production performance.

Unsolved Problems in MLOps - ACM Queue by Niall Murphy and Todd Underwood

Platforms

• Ray Distributed Computing - Anyscale

Frameworks

Chronon  is an open source end-to-end feature platform that allows Machine Learning (ML) teams to easily build, deploy, manage and monitor data pipelines for machine learning. It’s currently used to power all major ML applications within Airbnb, as well as major use cases at Stripe. Airbnb and Stripe jointly manage and maintain the project.

[via ](https://docs.google.com/document/d/10o75QTLfkaHCiiT2BftBld8ImuTojefZ03AYm__L9yw/edit) / [HN](https://news.ycombinator.com/item?id=41709547)

Blog posts

• MLOps is Mostly Data Engineering. • Kostas Heaven on Net

References

• Shankar, Garcia, Hellerstein, Parameswaran (2022). Operationalizing Machine Learning: An Interview Study.

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

• Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.

• Math Academy - Mathematics for Machine Learning.

• Machine Learning for MacroEconomics by Jesús Fernández-Villaverde | University of Pennsylvania

• Home | CS 229S; Systems for Machine Learning

• Math For Machine Learning [Resources] at https://lelouch.dev

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
• Leonie Monigatti, example see - 37 Things I Learned About Information Retrieval in Two Years at a Vector Database Company

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.

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See also: machine-learning-books