@cyrilXBT: MIT just quietly dropped a free AI curriculum that puts $50,000 university courses to shame. 12 books. Zero tuition. Fr…

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MIT has released a free AI curriculum comprising 12 textbooks covering foundations, advanced techniques, reinforcement learning, and ethics, offering a comprehensive education at no cost.

MIT just quietly dropped a free AI curriculum that puts $50,000 university courses to shame. 12 books. Zero tuition. From the same institution that produced the people building the models everyone is talking about. FOUNDATIONS 1. Foundations of Machine Learning — http://lnkd.in/gytjT5HC 2. Understanding Deep Learning — http://lnkd.in/dgcB68Qt 3. Machine Learning Systems — http://lnkd.in/dkiGZisg ADVANCED TECHNIQUES 4. Algorithms for ML — http://algorithmsbook.com 5. Deep Learning — http://lnkd.in/g2efT6DK REINFORCEMENT LEARNING 6. RL Basics (Sutton & Barto) — http://lnkd.in/guxqxcZZ 7. Distributional RL — http://lnkd.in/d4eNP-pe 8. Multi-Agent Systems — http://marl-book.com 9. Long Game AI — http://lnkd.in/g-WtzvwX ETHICS & PROBABILITY 10. Fairness in ML — http://fairmlbook.org 11. Probabilistic ML Part 1 — http://lnkd.in/g-isbdjj 12. Probabilistic ML Part 2 — http://lnkd.in/gJE9fy4w This is a complete MIT-level AI education. Not a YouTube playlist. Not a Twitter thread full of fluff. Textbooks written by the researchers who built the field. The people who actually study this will not just understand AI better than their peers. They will understand it better than most people currently getting paid to work in it. Most people will bookmark this and never open it. The ones who open it tonight are the ones who show up in 12 months having built something nobody around them understands yet. Bookmark this. Open the first one tonight. Follow @cyrilXBT for more resources that actually compound.
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Cached at: 06/11/26, 03:42 PM

MIT just quietly dropped a free AI curriculum that puts $50,000 university courses to shame.

12 books.

Zero tuition.

From the same institution that produced the people building the models everyone is talking about.

FOUNDATIONS

  1. Foundations of Machine Learning — http://lnkd.in/gytjT5HC
  2. Understanding Deep Learning — http://lnkd.in/dgcB68Qt
  3. Machine Learning Systems — http://lnkd.in/dkiGZisg

ADVANCED TECHNIQUES

  1. Algorithms for ML — http://algorithmsbook.com
  2. Deep Learning — http://lnkd.in/g2efT6DK

REINFORCEMENT LEARNING

  1. RL Basics (Sutton & Barto) — http://lnkd.in/guxqxcZZ
  2. Distributional RL — http://lnkd.in/d4eNP-pe
  3. Multi-Agent Systems — http://marl-book.com
  4. Long Game AI — http://lnkd.in/g-WtzvwX

ETHICS & PROBABILITY

  1. Fairness in ML — http://fairmlbook.org
  2. Probabilistic ML Part 1 — http://lnkd.in/g-isbdjj
  3. Probabilistic ML Part 2 — http://lnkd.in/gJE9fy4w

This is a complete MIT-level AI education.

Not a YouTube playlist.

Not a Twitter thread full of fluff.

Textbooks written by the researchers who built the field.

The people who actually study this will not just understand AI better than their peers.

They will understand it better than most people currently getting paid to work in it.

Most people will bookmark this and never open it.

The ones who open it tonight are the ones who show up in 12 months having built something nobody around them understands yet.

Bookmark this.

Open the first one tonight.

Follow @cyrilXBT for more resources that actually compound.

LinkedIn

Source: https://lnkd.in/gytjT5HC LinkedIn## This link will take you to a page that’s not on LinkedIn

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