Probabilistic Machine Learning
Probabilistic Machine Learning by Kevin Murphyby Kevin Murphy
Shares rank 5 of 10 machine learning books by recommenders
People recommend it as a thorough prerequisite for understanding the ideas behind modern deep learning, with well-regarded volumes covering deep learning and reinforcement learning.
People recommend it as a thorough prerequisite for understanding the ideas behind modern deep learning, with well-regarded volumes covering deep learning and reinforcement learning.
5 people gave an opinion
- recommend it
- 5
- criticize it
- 0
- approval share
- 100%
Recommended in 3 of 13 months
- November 2025: not recommended
- December 2025: not recommended
- January 2026: not recommended
- February 2026: not recommended
- March 2026: recommended
- April 2026: not recommended
- May 2026: recommended
- June 2026: recommended
- July 2026: not recommended
- August 2026: not recommended
- September 2026: not recommended
- October 2026: not recommended
- November 2026: not recommended (month in progress)
Why it doesn’t carry the badge
- 5 people recommend itNeeds 10 or more, not met.
- Nobody criticizes itNeeds 2 to 1 or better, met.
- Recommended in 3 of 13 monthsNeeds 4 or more, not met.
The same three rules apply to every product, and the badge can’t be bought. How the badge works
What people say about it
The reasons people give, with the number of comments that give each one. Open a reason to read what they said.
Why people recommend it
Each mark is one comment
Covers material needed for deep learning
“contains the material that I would consider prerequisite if you want to understand the ideas which underpin modern deep learning”
Comment from 16 Jun 2026 in “The 90-year-old idea behind JEPA models: Canonical Correlation Analysis” “Volumes 1 & 2 (2022-3) contain a substantial amount of deep learning”
Comment from 16 Jun 2026 in “The 90-year-old idea behind JEPA models: Canonical Correlation Analysis”
Other machine learning books people recommend
The four most recommended other machine learning books, with the same counts for each, so you can compare like for like. The tick on each bar marks the 2 to 1 line the badge needs.
| Product | Recommend | Criticize | Approval | Months | Owned | Badge |
|---|---|---|---|---|---|---|
| Build a Large Language Model (From Scratch)by Sebastian Raschka | 16 | 0 | 100% | 8 | — | Approved |
| Build an LLM from Scratchby Sebastian Raschka | 8 | 0 | 100% | 5 | — | — |
| Python Data Science Handbookby Jake VanderPlas | 8 | 0 | 100% | 2 | — | — |
| The Little Learnerby Daniel P. Friedman and Anurag Mendhekar | 6 | 2 | 75% | 2 | — | — |
| Probabilistic Machine Learningby Kevin MurphyThis page | 5 | 0 | 100% | 3 | — | — |
What people said
5 of the 6 comments that mention it, newest first. Each links to the original comment.
“These are very nice volumes though (RL one is good too), and Murphy should be commended for the amount of work in here.”
“Volumes 1 & 2 (2022-3) contain a substantial amount of deep learning”
“contains the material that I would consider prerequisite if you want to understand the ideas which underpin modern deep learning”
“Just read a good textbook instead of this LLM-written stuff.”
“Excellent quality paper, great printing. It's a keeper.”
Counts come from 1,795,640 Hacker News comments, November 2025 to November 2026. How we count. Report a mistake.