Machine learning
"This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online"--Back cover.
Stats for nerds
15recommend
31mentions
28people
2012–2025years
55% recommend45% neutral
Mentions per year
What Hacker News says
- For a latest reference on AI and machine learning for network engineer please check this book by Javier Antich [1].
Please also check the review here [2]. For what it's worth, the book is listed in the "10 Books Every Network Engineer Should Read" [3].
[1] Machine Learning for Network and Cloud Engineers: Get ready for the next Era of Network Automation:
https://www.goodreads.com/book/show/101180344-machine-learni...
[2] MUST READ: Machine Learning for Network and Cloud Engineers:
https://blog.ipspace.net/2023/02/machine-learning-network-cl...
[3] 10 Books Every Network Engineer Should Read:
https://networkphil.com/2024/05/21/10-books-every-network-en...
- it’s been a while since I’ve been a beginner so I might not have the best resources, but I would recommend Harvard’s Stat 110 with Joe Blitzstein (lectures online) and then Machine Learning by Kevin Murphy. might be a scarier book to someone not confident in their math, but overall a better one imo
for something more directly comparable to the niche ISLR filled, Bishop’s books are generally better - although I can’t recall their title
- It depends on the particular subtopics it covers. Machine Learning: A Probabilistic Perspective is from 2012 and it's still a great resource, although Murphy's newer book will certainly cover more up-to-date material.
- His old book did just that: https://www.amazon.com/Machine-Learning-Probabilistic-Perspe...
- Machine Learning: A Probabilistic Perspective by Kevin Murphy has a chapter on it. Or you can read the few papers from the 90s and early 00s on it.
- Unfortunately, it's just something you start to notice once you become more familiar with the fundamental math underlying all of this.
The book, Machine Learning: A Probabilistic Perspective by Kevin Murphy (the original book everyone in this thread is talking about) is probably the closest thing I can think of. Its goal is to frame everything around graphical models and probability. It's quite a tome. Still, despite its breadth, it can't possibly cover everything.
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