Answers.Body: <p><a href="https://sites.google.com/view/berkeley-cs294-158-sp20/home" rel="nofollow noreferrer">Berkeley CS294-158</a> is a graduate-level course on deep unsupervised learning. They cover a lot of architectures used in modern generative modeling. They have recorded lectures and slides online.</p>
  <p><a href="https://deepgenerativemodels.github.io/" rel="nofollow noreferrer">Stanford CS236</a> is a course on deep generative modeling. They don't have a textbook, but they have course notes and slides online.</p>
  
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Answers.Text: What part of "I'm looking for a modern machine learning **book**" is not understandable?
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Answers.Text: A good resource is a good resource. I doubt the medium that resource comes in is a deal-breaker for someone studying for an exam. *Especially* because they're looking for resources on relatively recent topics (e.g., diffusion in the context of deep generative modeling only came out in 2020). Good quality textbooks aren't published in three years -- course notes are going to be close to the best resource you can get.
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Answers.Text: I totally understand what you're getting at, but I thought it'd be more helpful to at least point them in the direction of helpful resources instead of just leaving the question answered.
Answers.UserDisplayName: Alexander Wan
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Answers.Text: Let's put it in this way. Let's say I'm looking for the Lord of the Rings films and instead you give me the book. Thanks a lot, but that's not what I ask - I don't like reading. Anyway, the OP will decide if your answer is fine.
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Answers.Text: @AlexanderWan Thanks for the links, I guess it is a good material for self-study, but this is not what  I'm looking for. Anyways, upvoted.
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Answers.Text: @AlexanderWan, usually lecture notes become books at some point. I believe  the book that I linked was lecture  notes that were properly bundled together into a formal book that could be referenced.
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Answers.Body: <p>Here are my suggestions:</p>
  <ol>
  <li><strong><a href="https://www.bishopbook.com" rel="nofollow noreferrer">Deep Learning: Foundations and Concepts</a></strong> (2024) by Bishop – A comprehensive and up-to-date text covering fundamental and advanced topics in deep learning.</li>
  <li><strong><a href="http://incompleteideas.net/book/the-book-2nd.html" rel="nofollow noreferrer">Reinforcement Learning: An Introduction</a></strong> by Sutton – A classic and authoritative reference on reinforcement learning, perfect for understanding RL fundamentals and recent advances.</li>
  <li><strong><a href="https://www.marl-book.com" rel="nofollow noreferrer">Multi-Agent Reinforcement Learning: Foundations and Modern Approaches</a></strong> (2024) – Focuses on the growing field of multi-agent systems, with a solid theoretical foundation and modern applications.</li>
  <li><strong><a href="https://deeplearningtheory.com" rel="nofollow noreferrer">The Principles of Deep Learning Theory</a></strong> – Offers a theoretical perspective on deep learning, bridging the gap between statistical physics and machine learning.</li>
  <li><strong><a href="https://mitpress.mit.edu/9780262048972/foundations-of-computer-vision/" rel="nofollow noreferrer">Foundations of Computer Vision</a></strong>  (2024) – A modern take on computer vision, providing both foundational knowledge and cutting-edge techniques.</li>
  </ol>
  <p>Also, don't forget the famous <a href="https://www.lesswrong.com/posts/t4ZBjAjXk2NqqAqJ7/the-27-papers" rel="nofollow noreferrer">list of 27 papers</a> (reportedly shared by Ilya Sutskever with John Carmack). As Sutskever mentioned:</p>
  <blockquote>
  <p><em>&quot;If you truly master these, you’ll understand 90% of what matters in modern AI.&quot;</em></p>
  </blockquote>
  
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Body: <p>I'm looking for a modern machine learning book with graduate-level treatment of more recent topics such as diffusion and generative models, transformers etc.</p>
  <p>I have a hard copy of <em>Deep Learning</em> by Goodfellow and Bengio; while I liked the book and read it extensively when it was published, it is a bit dated now.</p>
  <p>I'm considering <em><a href="https://probml.github.io/pml-book/book2.html" rel="nofollow noreferrer">Probabilistic Machine Learning: Advanced Topics</a></em> by Kevin Patrick Murphy. But maybe there are better alternatives.</p>
  <p>I need this for my comprehensive Ph.D. examination. Any suggestions are very appreciated.</p>
  <p>P.S. I'm also aware of this <a href="https://ai.stackexchange.com/questions/23507/what-are-other-examples-of-theoretical-machine-learning-books">stack post</a>, but the list of references there is quite dated or introductory.</p>
  
Tags: <reference-request><transformer><academia><diffusion-models>
Title: Modern graduate-level machine learning books with focus on generative models
UserDisplayName: user74376
ThreadText: # Modern graduate-level machine learning books with focus on generative models
  
  (Asked by: username_0 on 2023-07-24)
  
  I'm looking for a modern machine learning book with graduate-level treatment of more recent topics such as diffusion and generative models, transformers etc.
  
  I have a hard copy of *Deep Learning* by Goodfellow and Bengio; while I liked the book and read it extensively when it was published, it is a bit dated now.
  
  I'm considering *[Probabilistic Machine Learning: Advanced Topics](https://probml.github.io/pml-book/book2.html)* by Kevin Patrick Murphy. But maybe there are better alternatives.
  
  I need this for my comprehensive Ph.D. examination. Any suggestions are very appreciated.
  
  P.S. I'm also aware of this [stack post](https://ai.stackexchange.com/questions/23507/what-are-other-examples-of-theoretical-machine-learning-books), but the list of references there is quite dated or introductory.
  
  **Tags:** reference-request, transformer, academia, diffusion-models
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2023-07-24. Score: 2
  
  [Berkeley CS294-158](https://sites.google.com/view/berkeley-cs294-158-sp20/home) is a graduate-level course on deep unsupervised learning. They cover a lot of architectures used in modern generative modeling. They have recorded lectures and slides online.
  
  [Stanford CS236](https://deepgenerativemodels.github.io/) is a course on deep generative modeling. They don't have a textbook, but they have course notes and slides online.
  
  > **Comments:**
  > **username_2**: What part of "I'm looking for a modern machine learning **book**" is not understandable?  
  > **username_1**: A good resource is a good resource. I doubt the medium that resource comes in is a deal-breaker for someone studying for an exam. *Especially* because they're looking for resources on relatively recent topics (e.g., diffusion in the context of deep generative modeling only came out in 2020). Good quality textbooks aren't published in three years -- course notes are going to be close to the best resource you can get.  
  > **username_1**: I totally understand what you're getting at, but I thought it'd be more helpful to at least point them in the direction of helpful resources instead of just leaving the question answered.  
  > **username_2**: Let's put it in this way. Let's say I'm looking for the Lord of the Rings films and instead you give me the book. Thanks a lot, but that's not what I ask - I don't like reading. Anyway, the OP will decide if your answer is fine.  
  > **username_0**: @AlexanderWan Thanks for the links, I guess it is a good material for self-study, but this is not what  I'm looking for. Anyways, upvoted.  
  > **username_0**: @AlexanderWan, usually lecture notes become books at some point. I believe  the book that I linked was lecture  notes that were properly bundled together into a formal book that could be referenced.  
  
  ---
  
  ### Answer by username_3 on 2024-10-07. Score: 1
  
  Here are my suggestions:
  
  1. **[Deep Learning: Foundations and Concepts](https://www.bishopbook.com)** (2024) by Bishop – A comprehensive and up-to-date text covering fundamental and advanced topics in deep learning.
  2. **[Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book-2nd.html)** by Sutton – A classic and authoritative reference on reinforcement learning, perfect for understanding RL fundamentals and recent advances.
  3. **[Multi-Agent Reinforcement Learning: Foundations and Modern Approaches](https://www.marl-book.com)** (2024) – Focuses on the growing field of multi-agent systems, with a solid theoretical foundation and modern applications.
  4. **[The Principles of Deep Learning Theory](https://deeplearningtheory.com)** – Offers a theoretical perspective on deep learning, bridging the gap between statistical physics and machine learning.
  5. **[Foundations of Computer Vision](https://mitpress.mit.edu/9780262048972/foundations-of-computer-vision/)** (2024) – A modern take on computer vision, providing both foundational knowledge and cutting-edge techniques.
  
  Also, don't forget the famous [list of 27 papers](https://www.lesswrong.com/posts/t4ZBjAjXk2NqqAqJ7/the-27-papers) (reportedly shared by Ilya Sutskever with John Carmack). As Sutskever mentioned:
  
  > *"If you truly master these, you’ll understand 90% of what matters in modern AI."*
  
  
  ---
  
  