If you want to learn artificial intelligence, machine learning, deep learning, reinforcement learning, computer vision, AI fairness, or production ML, you do not need to start by buying expensive textbooks. MIT Press and author-hosted open-access editions make many excellent AI and ML books available online for free.
Updated: May 2026
Best Learning Path
Not sure where to start? Use this path:
- Beginner-friendly foundation: Probabilistic Machine Learning: An Introduction
- Deep learning basics: Understanding Deep Learning
- Classic deep learning reference: Deep Learning
- Decision-making and RL: Reinforcement Learning and Algorithms for Decision Making
- Advanced AI systems: Multi-Agent Reinforcement Learning and Distributional Reinforcement Learning
- Responsible and real-world ML: Fairness and Machine Learning plus Machine Learning in Production
Completely Free AI & ML Books
1. Understanding Deep Learning
Author: Simon J. D. Prince
A modern, visual, and accessible deep learning textbook. Great for learners who want explanations that connect theory with practice.
- Neural networks
- Optimization
- Transformers and modern deep learning ideas
- Python notebooks and figures
2. Deep Learning
Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville
One of the most famous deep learning textbooks. Best for readers who want a broad reference covering the math, methods, and research foundations behind deep learning.
- Linear algebra and probability background
- Feedforward networks
- Convolutional networks
- Sequence modeling and generative models
3. Probabilistic Machine Learning: An Introduction
Author: Kevin P. Murphy
A detailed introduction to machine learning through probability, Bayesian decision theory, and modern ML methods.
- Supervised learning
- Unsupervised learning
- Deep learning foundations
- Bayesian modeling
4. Probabilistic Machine Learning: Advanced Topics
Author: Kevin P. Murphy
The advanced follow-up for readers who want deeper coverage of generative models, Bayesian inference, causality, and decision-making under uncertainty.
- Deep generative models
- Graphical models
- Bayesian inference
- Reinforcement learning and causality
5. Fairness and Machine Learning
Authors: Solon Barocas, Moritz Hardt, Arvind Narayanan
A must-read for understanding the opportunities, limits, and risks of automated decision-making systems.
- Algorithmic fairness
- Bias and discrimination
- Ethics of machine learning
- Social impact of automated decisions
6. Machine Learning in Production
Author: Christian Kästner
A practical guide for building real software products with machine learning components, not just training models in notebooks.
- ML product design
- MLOps and deployment
- Quality assurance
- Responsible ML engineering
7. Foundations of Computer Vision
Authors: Antonio Torralba, Phillip Isola, William T. Freeman
A modern computer vision book from leading researchers. It combines classic vision ideas with deep learning, transformers, diffusion models, fairness, and ethics.
- Image formation
- Recognition and representation
- Deep learning for vision
- Modern vision systems
8. Learning Theory from First Principles
Author: Francis Bach
A mathematically rigorous introduction to learning theory, optimization, statistical learning, and modern ML foundations.
- Statistical learning theory
- Optimization theory
- Generalization
- Modern overparameterized models
9. Reinforcement Learning: An Introduction
Authors: Richard S. Sutton, Andrew G. Barto
The classic textbook for reinforcement learning. Ideal for understanding agents, rewards, value functions, policies, and learning through interaction.
- Value-based methods
- Policy gradients
- Function approximation
- Deep RL foundations
10. Algorithms for Decision Making
Authors: Mykel J. Kochenderfer, Tim A. Wheeler, Kyle H. Wray
A broad introduction to decision-making under uncertainty, including probabilistic reasoning, planning, reinforcement learning, and multi-agent settings.
- Bayesian networks
- Sequential decision problems
- Planning under uncertainty
- Reinforcement learning
11. Multi-Agent Reinforcement Learning
Authors: Stefano V. Albrecht, Filippos Christianos, Lukas Schäfer
A modern textbook on how multiple learning agents interact in shared environments, combining reinforcement learning, deep learning, and game theory.
- MARL foundations
- Game-theoretic models
- Deep multi-agent RL
- Python code and slides
12. Distributional Reinforcement Learning
Authors: Marc G. Bellemare, Will Dabney, Mark Rowland
A specialized reinforcement learning book focused on modeling the full distribution of returns, not only expected rewards.
- Distributional RL theory
- Random returns
- Risk-sensitive decision-making
- Advanced reinforcement learning methods
Quick Comparison Table
| Book | Best For | Level |
|---|---|---|
| Understanding Deep Learning | Modern deep learning with visual explanations | Beginner to Intermediate |
| Deep Learning | Classic deep learning reference | Intermediate to Advanced |
| Probabilistic Machine Learning: An Introduction | Core ML through probability | Intermediate |
| Probabilistic Machine Learning: Advanced Topics | Generative models, Bayesian inference, causality | Advanced |
| Fairness and Machine Learning | Responsible AI and algorithmic fairness | Intermediate |
| Machine Learning in Production | Deploying ML in real software products | Intermediate |
| Foundations of Computer Vision | Computer vision and deep learning for vision | Beginner to Intermediate |
| Reinforcement Learning | Classic RL foundations | Intermediate to Advanced |
How to Use These Books
Do not try to read every book cover to cover at once. Pick one path based on your goal:
- Want to become an ML engineer? Start with Probabilistic Machine Learning, then Machine Learning in Production.
- Want to learn deep learning? Start with Understanding Deep Learning, then use Deep Learning as a reference.
- Want to learn AI for robotics or agents? Read Algorithms for Decision Making, then Reinforcement Learning.
- Want to build responsible AI systems? Read Fairness and Machine Learning alongside any technical ML book.
- Want to study modern vision models? Read Foundations of Computer Vision.
FAQ
Are these AI and ML books really free?
Yes, the links above point to open-access or author-hosted free online versions. Some books also have paid print or ebook editions, but the online reading versions are available at no cost.
Are all of these books directly from MIT?
These are MIT Press books or MIT/MIT-author-hosted open resources. Some free editions are hosted by the authors rather than directly on the MIT Press website.
Which book should beginners start with?
For deep learning beginners, start with Understanding Deep Learning. For general machine learning, start with Probabilistic Machine Learning: An Introduction.
Do I need strong math before reading these?
Some books require linear algebra, probability, calculus, and optimization. If you are new, start with beginner-friendly chapters and use the advanced books as references.
Final Thoughts
This is one of the best free AI and machine learning reading lists available online. Whether you are a beginner, student, researcher, engineer, or self-taught developer, these MIT Press and open-access books can help you build a serious foundation in artificial intelligence and machine learning without spending money on expensive textbooks.
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