<story><title>Probabilistic Machine Learning: An Introduction</title><url>https://probml.github.io/pml-book/book1.html</url></story><parent_chain></parent_chain><comment><author>joaorico</author><text>The new edition has been split in two parts. The pdf draft (921 pages) and python code [1] of the first part are now available. The table of contents of the second part is here [2].&lt;p&gt;From the preface:&lt;p&gt;&amp;quot;By Spring 2020, my draft of the second edition had swollen to about 1600 pages, and I was still not
done. At this point, 3 major events happened. First, the COVID-19 pandemic struck, so I decided
to “pivot” so I could spend most of my time on COVID-19 modeling. Second, MIT Press told me
they could not publish a 1600 page book, and that I would need to split it into two volumes. Third,
I decided to recruit several colleagues to help me finish the last ∼ 15% of “missing content”. (See
acknowledgements below.)&lt;p&gt;The result is two new books, “Probabilistic Machine Learning: An Introduction”, which you are
currently reading, and “Probabilistic Machine Learning: Advanced Topics”, which is the sequel to
this book [Mur22].&lt;p&gt;Together these two books attempt to present a fairly broad coverage of the field
of ML c. 2020, using the same unifying lens of probabilistic modeling and Bayesian decision theory
that I used in the first book.
Most of the content from the first book has been reused, but it is now split fairly evenly between
the two new books. In addition, each book has lots of new material, covering some topics from deep
learning, but also advances in other parts of the field, such as generative models, variational inference
and reinforcement learning. To make the book more self-contained and useful for students, I have
also added some more background content, on topics such as optimization and linear algebra, that
was omitted from the first book due to lack of space.&lt;p&gt;Another major change is that nearly all of the software now uses Python instead of Matlab.&amp;quot;&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;probml&amp;#x2F;pyprobml&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;probml&amp;#x2F;pyprobml&lt;/a&gt;&lt;p&gt;[2] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;probml.github.io&amp;#x2F;pml-book&amp;#x2F;book2.html&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;probml.github.io&amp;#x2F;pml-book&amp;#x2F;book2.html&lt;/a&gt;</text></comment>
<story><title>Probabilistic Machine Learning: An Introduction</title><url>https://probml.github.io/pml-book/book1.html</url></story><parent_chain></parent_chain><comment><author>JHonaker</author><text>This is probably my favorite introductory machine learning book. The fact that he places almost everything in the language of graphical models is such a good common ground to build off.&lt;p&gt;This really sets you up to realize that there is (and should be) a lot more to doing a good job in machine learning than simply minimizing an objective function. The answers you get depend on the model you create as do the questions you can hope to answer.&lt;p&gt;I don&amp;#x27;t see a clear list of differences between this new edition. Does anyone know what&amp;#x27;s new?</text></comment>