Machine Learning

• Machine Learning Systems - Principles and Practices of Engineering Artificially Intelligent Systems by Vijay Janapa Reddi
  • extension of the CS249r course at Harvard University, taught by Prof. Vijay Janapa Reddi
  • Clipped: Machine Learning Systems Last Updated: January 12, 2025
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman
  • this is a cheat code
• CS229: Machine Learning by Tengyu Ma, Andrew Ng and Chris Ré
• A Course in Machine Learning by Hal Daumé III
• Unsupervised Feature Learning and Deep Learning Tutorial
• Oxford Machine Learning by Nando de Freitas
• An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
• Statistical Learning with Sparsity by Trevor Hastie, Robert Tibshirani and Martin Wainwright
• Pattern Recognition and Machine Learning by Christopher Bishop
• Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar
• Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
• Statistical Modeling and Analysis of Neural Data (Spring 2018) by Jonathan Pillow
• Theoretical Machine Learning (Princeton Computer Science 511; Spring 2014) by Rob Schapire
• Shervine Amidi: Teaching - ML Cheat Sheets
• How to Train Your Robot by Brandon Rohrer - a book-in-progress about applied robotics, machine learning, and software engineering
• Machine Learning @Sapienza - Course material, 2nd semester a.y. 2023/2024, Mathematical Sciences for AI taught by Emanuele Rodolà
• rushter/MLAlgorithms: Minimal and clean examples of machine learning algorithms implementations
• https://probml.github.io/pml-book/book1.html
• Probabilistic Machine Learning: An Introduction (March 2022; draft PDF) by Kevin Patrick Murphy

Production-oriented

Apple’s Machine learning-powered APIs

These are much more production and creativity-oriented $\to$ allowing developers to build the features / think of the use cases

• Apple Developer Machine Learning - Bring intelligent on-device machine learning powered features, object detection in images and video, language analysis, and sound classification, to your app with just a few lines of code.
• Core ML- Core ML delivers blazingly fast performance on Apple devices with easy integration of machine learning and AI models into your apps. Convert models from popular training libraries using Core ML Tools or download ready-to-use Core ML models. Easily preview models and understand their performance right in Xcode.
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