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Probabilistic Machine Learning: An Introduction

probml.github.io

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Re: Probabilistic Machine Learning: An Introduction

#11

Post by hnracer »

master_yoda_1 wrote: post #10
JHonaker wrote: post #5

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. 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 y…

Agree with you. But none of this is useful for practical (applied) machine learning. I don't want to disappoint you but you can read it as machine learning porn, but otherwise don't waste time on it.

Are you saying the book itself is ML porn?

Re: Probabilistic Machine Learning: An Introduction

#12

Post by abhinav22 »

For anybody truly serious about this field, I recommend the below book. It has some poor reviews on Amazon, which I was shocked to see, but it is my favourite book and taught me the core of probability theory and statistics, in a way most books don’t. Your understanding of Machine Learning will be better than 90% of those out there, if you can get through the principles in this book.

I topped statistics at the most prestigious university in my country both at the undergrad and postgrad level, and had no problem discussing advanced concepts with Senior PHDs in Quantitative Fields, and I thank this book the most for beginning my journey on this. But, and this is important, make sure to do all the exercises!

https://www.amazon.com/John-Freunds-Mathematical-Statistics-...

Re: Probabilistic Machine Learning: An Introduction

#15

Post by jbay808 »

joaorico wrote: post #7

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]. From the preface: "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 s…

It's very encouraging to see Matlab losing ground in the educational space. I don't know why so many engineers let their foundational skills to be locked behind a proprietary ecosystem like that.

Re: Probabilistic Machine Learning: An Introduction

#16

Post by master_yoda_1 »

hnracer wrote: post #11
master_yoda_1 wrote: post #10

Earlier quoted context omitted.

Agree with you. But none of this is useful for practical (applied) machine learning. I don't want to disappoint you but you can read it as machine learning porn, but otherwise don't waste time on it.

Are you saying the book itself is ML porn?

Yes, unless you are among 20 top researchers who are working on frontier of ml. Bayesian probabilistic techniques does not work or are very slow for any practical purpose.

Re: Probabilistic Machine Learning: An Introduction

#17

Post by master_yoda_1 »

saeranv wrote: post #8

Looking at the table of contents (for someone who is not familiar with the term 'Probabilistic Machine Learning'), is this just covering typical ML methods through the lens of probability?

Answer is not so black and white as everything in ml has to use probability. You can ignore this unless you are among 20 top researchers who are working on frontier of ml. Bayesian probabilistic techniques does not work or are very slow for any practical purpose.

Re: Probabilistic Machine Learning: An Introduction

#20

Post by amkkma »

master_yoda_1 wrote: post #17
saeranv wrote: post #8

Looking at the table of contents (for someone who is not familiar with the term 'Probabilistic Machine Learning'), is this just covering typical ML methods through the lens of probability?

Answer is not so black and white as everything in ml has to use probability. You can ignore this unless you are among 20 top researchers who are working on frontier of ml. Bayesian probabilistic techniques does not work or are very slow for any practical purpose.

But does it aid in understanding regular models, as they might have a bayesian interpretation?
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