Machine Learning & Deep Learning

Jump to bottom

Carlos Lizarraga-Celaya edited this page Jul 3, 2026 · 16 revisions

Machine Learning & Deep Learning

Available resources online.

Publications

ML Papers Explained

Online Books.

Classical Machine Learning

A Brief Introduction to Machine Learning for Engineers. Osvaldo Simeone.
A Comprehensive Guide to Machine Learning. Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang, UC Berkeley.
A course in Machine Learning. Hal Daumé III.
Algorithms for Artificial Intelligence. Robert J. Moss, Stanford.
[Algorithms for Optimization](https://algorithmsbook.com/optimization/]. Mykel J. Kochenderfer, Tim A. Wheeler. MIT Press.
Algorithms for Decision Making. Mykel J. Kochenderfer, Tim A. Wheeler,and Kyle H. Wra. MIT Press.
Algorithms for Validation. Mykel J. Kochenderfer, Sydney M. Katz, Anthony L. Corso, and Robert Moss. MIT Press.
Approaching (almost) any Machine Learning Problem. Abhishek Thakur.
Bayesian Modeling and Computation in Python. Martin Osvaldo A, Kumar Ravin; Lao Junpeng, 2021.
Big Data and AI Strategies. Marco Kolanovic, Rajesh T. Krishnamachari. JPMorgan Chase & Co.
Cluster Analysis, 5th. Edition. Brian S. Everitt, Sabine Landau, Morven Leese, and Daniel Stahl
CS229: Machine Learning. Andrew Ng, Tengyu Ma. Stanford University.
Concise Machine Learning. Jonathan Richard Shewchuk, UC Berkeley.
Data Clustering. Algorithms and Applications. Charu C. Aggarwal and Chandan K. Reddy Editors.
Data Science and Machine Learning. Mathematical and Statistical Methods. Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman.
Designing Machine Learning Systems. Chip Huyen.
Fairness and machine learning. Limitations and Opportunities. Solon Barocas, Moritz Hardt, Arvind Narayanan.
Foundations of Machine Learning. 2nd. ed. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar.
Hands-on in Machine Learning with Scikit-Learn, Keras & Tensorflow, 3rd. Ed., 2nd. Ed. (ML Notebooks). Aurélien Géron.
Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. Christoph Molnar.
Introduction to Machine Learning. Alex Smola and S.V.N. Vishwanathan.
Introduction to Machine Learning with Python. Andreas Muller and Sarah Guido.
Intuitive Machine Learning. Vincent Granville.
Machine Learning Engineering. Andriy Burkov.
Machine Learning for Developers. Goku Mohandas.
Machine Learning from Scratch. Derivations in Concepts and Code. Danny Friedman.
Machine Learning in Action.Peter Harrington.
Machine Learning Systems. Vijay Janapa Reddi.
Machine Learning with PyTorch and Scikit-Learn Book. Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili.
Machine Learning Visualized. Gavin Hung.
Machine Learning with TensorFlow.js. Annan Hashmi.
Machine Learning Yearning. Andrew Ng.
Mathematics for Machine Learning. Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.
Mathematics for Machine Learning. Garret Thomas. UC Berkeley.
Mining of Massive Data. Jure Leskovec, Anand Rajaraman, Jeff Ullman.
Model-based Machine Learning. John Winn with Christopher M. Bishop, Thomas Diethe, John Guiver, and Yordan Zaykov.
Network Science. Albert-László Barabási.
Notes on Data Science & Machine Learning. Chris Albon.
Pattern Recognition and Machine Learning. Christopher Bishop.
Patterns, Predictions, and Actions. A History about Machine Learning. Morris Hardt and Benjamin Recht.
Practical Machine Learning with PyTorch. Atkinson, & Denholm. Journal of Open Source Education, 7(76), 239. |Code.
Principles and Practices of Machine Learning Systems Engineering. Vijay Janapa Reddi.
Probabilistic Artificial Intelligence. Andreas Krause, Jonas Hübotter.
Probabilistic Machine Learning: An Introduction. Kevin P. Murphy.
Probabilistic Machine Learning: Advanced Topics. Kevin P. Murphy.
Python Data Science Handbook. Jake VanderPlas.
Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow 2. Sebastian Raschka and Vahid Mirkalili.
Python Machine Learning Book Code Repository. Sebastian Raschka.
Python Machine Learning By Example 3rd. Ed., Book Code Repository. Yuxi (Hayden) Liu.
Reinforcement Learning: An Introduction. 2nd. ed. Richard S. Sutton and Andrew G. Barto.
Rules of Machine Learning: Best Practices for ML Engineering. Martin Zinkevich.
Scrapbook. Unorganized Notes. Stephan Osterburg.
Speech and Language Processing (3rd ed. draft). Dan Jurafsky and James H. Martin.
Statistics and Machine Learning in Python. Edouard Duchesnay, Tommy Löfstedt, Feki Younes.
Text Mining with R. Julia Silge and David Robinson.
The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
The Hundred-Page Machine Learning Book. Andriy Burkov.
The Illustrated Machine Learning Website. Francesco di Salvo, Mateo Bernabito, Simone Raponi.
Understanding Machine Learning: From Theory to Algorithms. Shai Shalev-Shwartz and Shai Ben-David.
Unraveling Principal Component Analysis. Peter Bloem.

Deep Learning

Agents in the Long Game of AI. Marjorie McShane, Sergei Nirenburg, and Jesse English.
AI Engineering. Chip Huyen.
Alice’s Adventures in a differentiable wonderland. A Primer on designing neural networks. Simone Scardapane.
Deep Learning. I. Goodfellow, Y. Bengio and A. Courville.
Deep Learning. Foundations and Concepts. Christopher M. Bishop, Hugh Bishop.
Deep Learning for Natural Language Processing: A Gentle Introduction. Mihai Surdeanu and Marco A. Valenzuela-Escárcega.
Distributional Reinforcement Learning. Marc G. Bellemare, Will Dabney, and Mark Rowland.
Dive into Deep Learning. Interactive deep learning book with code, math, and discussions. Various authors.
Hands-On Large Language Models. Jay Alammar and Maarten Grootendorst.
The Hitchhiker's Guide to Agentic AI: From Foundations to Systems. Haggai Roitman.
Introduction to Graph Neural Networks for Machine Learning Engineers. James H. Tanis, Chris Giannella, Adrian V. Mariano, and Daoud Meerzaman.
Introduction to Reinforcement Learning. Richard S. Sutton, Andrew G. Barto.
Machine and Deep Learning Compendium. Ori Cohen.
Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory. Arnulf Jentzen, Benno Kuckuck and Philippe von Wurstemberger.
Mathematical theory of deep learning. Philipp Petersen, Jakob Zech. arXiv:2407.18384.
Mathematics for Neural Networks. Bart M.N. Smets.
ML Papers Explained. DAIR-AI, Democratizing Artificial Intelligence Research, Education, and Technologies.
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. Stefano V. Albrecht, Filippos Christianos, and Lukas Schäfer.
Natural Language Processing. Jacob Einsenstein.
Natural Language Processing with Python. Steven Bird, Ewan Klein, and Edward Loper.
Neural Networks and Deep Learning. Michael Nielsen.
Papers with Code: free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables.
Physics-based Deep Learning. N. Thuerey, P. Holl, M. Mueller, P. Schnell, F. Trost, K. Um.
Practical Deep Learning for Coders | Book. Jeremy Howard.
Principles of Deep Learning Theory. Daniel A. Roberts, Sho Yaida, Boris Hanin.
Programming PyTorch for Deep Learning. Creating and Deploying Deep Learning Applications. Ian Pointer.
Reinforcement Learning. An introduction. 2nd. Ed. ( Lisp code | Python code | References.). Richard S. Sutton, Andrew G. Barto.
Reinforcement Learning: A Comprehensive Overview. Kevin Murphy.
SamGeo. Segment Anything Model for geospatial data. Wu, Q., Osco, L.
Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models. 3rd. Ed. Daniel Jurafsky, James H. Martin.
The Engineer's Guide to Deep Learning. Hironobu Suzuki.
The Little Book of Deep Learning. François Fleuret.
The Principles of Deep Learning Theory. Daniel A. Roberts, Sho Yaida, Boris Hanin. Cambridge University Press.
Transformers for Natural Language Processing. 2nd. Ed. Denis Rothman.
Understanding Deep Learning. Simon J.D. Prince.
Zero to Mastery TensorFlow for Deep Learning Book. Daniel Bourke.

Open Courses

AI Watchlist. Distilled AI.
Applications of Deep Neural Networks. T81 558, Jeff Heaton. Washington University in Saint Louis.
Applied Machine Learning. 2019 Fall CS5785 Cornell Tech.
Distilled AI. Courses, Primers, Research, Coding, Lists.
Full Stack Machine Learning. Full Stack Deep Learning.
Introduction to Deep Learning. Yann LeCun, NYU.
Google Machine Learning Education. Foundational courses by Google.
Introduction to Machine Learning. Simpl¡Learn.com.
Reinforcement Learning Course. David Silver.
MIT 6.S191: Introduction to Deep Learning. Alexander Amini.

Microsoft AI lessons

PyTorch

PyTorch Tutorials.
Deep Learning Fundamentals (PyTorch Lightning). Sebastian Raschka.
Programming PyTorch for Deep Learning. Ian Pointer.

Jupyter Notebooks Examples.

Scikit-Learn Tutorial. Jake VanderPlas.

Other

Best of Machine Learning with Python
Machine Learning Glossary
Machine Learning Glossary
Awesome Machine Learning Visualizations (in Github). Mohamed Kedir Noordeen.
Cheat Sheets for Machine Learning and Data Science. Aqeel Anwar.
ConvNetJS: Deep Learning in your browser
Mathematics for Machine Learning - dependency structure of knowledge.

Updated: 07/03/2026 (C. Lizarraga)

University of Arizona. Data Science Institute, 2025.