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1 point

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19th Sep 2020

Hello I think that a good book about probabilistic ML is this :

It seems to me that the author has make good attempt to explain complicated theories in simplified manner.

It covers the Bayesian approach that of course is not the entire statistics applied to ML but in my opinion it clarifies a lot of concepts

1 point

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7th Aug 2020

Bishops Pattern Recognition and Machine Learning comes at it from an entirely mathematical/inferential statistical perspective. It's a fantastic book, the first chapter alone involving probability theory is a good introduction to Bayesian statistics.

It assumes a working knowledge of linear algebra, as well as calc (mutl-variable and vector calculus). Some knowledge of differential equations go a long way as well but aren't essential, per se.

1 point

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3rd May 2018

Well there an actual book called Pattern Recognition and Machine Learning. Have you had a look at this?

1 point

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6th Feb 2017

/r/Nader_Nazemi please do your homework. This question has been asked a dozen at least in the past years. Likewise, a quick Google search will find you that he wrote on of the popular ML books

1 point

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4th Mar 2015

A lot of techniques in machine learning can be described from a Bayesian perspective, as evinced in one of the most popular textbooks in the field.

> it is still a current research topic

Absolutely, e.g. Bayesian nonparametrics among many many others.

The argument regarding the date that some underlying principles of Bayesian analysis were discovered is moot, it's like saying computer graphics is not that relevant since geometry originated in Euclid's works in 300 BC.

1 point

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17th Nov 2010

That's not a bad idea at all - I used EM way back (like 2002) for natural language processing, still remember it a bit, but dang gonna have to brush up :) Thx for the pointer!

Edit: Haha just realized I have that book! Recognized it from the cover shot on amazon :)

29 points

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22nd Jul 2019

Having done an MEng at Oxford where I dabbled in ML, the 3 key texts that came up as references in a lot of lectures were these:

Pattern Recognition and Machine Learning (Information Science and Statistics) (Information Science and Statistics) https://www.amazon.co.uk/dp/0387310738/ref=cm_sw_r_cp_apa_i_TZGnDb24TFV9M

Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning Series) https://www.amazon.co.uk/dp/0262018020/ref=cm_sw_r_cp_apa_i_g1GnDb5VTRRP9

(Pretty sure Murphy was one of our lecturers actually?)

Bayesian Reasoning and Machine Learning https://www.amazon.co.uk/dp/0521518148/ref=cm_sw_r_cp_apa_i_81GnDbV7YQ2WJ

There were ofc others, and plenty of other sources and references too, but you can't go buying dozens of text books, not least cuz they would repeat the same things. If you need some general maths reading too then pretty much all the useful (non specialist) maths we used for 4 years is all in this: Advanced Engineering Mathematics https://www.amazon.co.uk/dp/0470646136/ref=cm_sw_r_cp_apa_i_B5GnDbNST8HZR

2 points

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6th Jul 2019

If you're worried about not doing projects and participating in Kaggle competitions, why not do those things? They're pretty low risk and high reward. If you're feeling shaky on the theory, read papers and reference textbooks, take notes, and implement things that interest you. For deep learning stuff there are some good resources here: https://github.com/ChristosChristofidis/awesome-deep-learning. For more traditional methods you can't go wrong with Chris Bishop's book (try googling it for a cheaper alternative to Amazon ;): https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738. Side projects can really help here, and the key is to use references, but don't just copy-paste. Think of something you'd like to apply machine learning to with a reasonable scope. Search google scholar/arxiv for papers that do this or something similar, read them, and learn the techniques. For reading research papers in an area where you're not extremely knowledgeable, use the references in the text or google things you don't know and make sure you understand so you could teach someone else. If you're interested in the topic and exhausted the references, go up the tree and use google scholar to find papers that list the one you're reading as a reference - you usually find interesting applications or improvements on the technique. You can also often find open source training data in the appendices of papers. Kaggle also has a ton of datasets, including obviously the ones they provide for competitions.

2 points

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10th Oct 2015

Nonconvex optimization is literally any optimization that's not convex (ed: fixed typo). It's like being advised to learn about "mitochondria" and also "all biology topics other than mitochondria."

There are a few nonconvex problems we can actually optimize (such as geometric programming and eigenvalue problems), but in general if it's nonconvex you're usually out of luck in terms of true optimization.

If you're getting into machine learning, I'd look for machine learning textbooks in particular. They tend to involve optimization techniques for nonconvex problems that won't truly __optimize__ nonconvex functions, but are generally applicable and in many cases can find solutions that are "almost" optimal, or at least pretty good, as long as the function isn't too wacky.

I've been out of the machine learning field for a few years now, but I recall Pattern Recognition and Machine Learning by Bishop and Elements of Statistical Learning by Hastie, Tibshirani, and Friedman to be pretty good.

1 point

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28th Apr 2022

In terms of actual statistics knowledge (not necessarily how to build ML predictive models), An Introduction to Statistical Learning covers the essentials that I think you'd need for most interviews. There's also a more advanced book, The Elements of Statistical Learning.

I also found Pattern Recognition and Machine Learning to be very good.

1 point

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26th Oct 2021

If you're more into books, checkout:

Machine learning books:

An Introduction to Statistical Learning: https://www.statlearning.com/

Pattern Recognition and Machine Learning: https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738

Machine Learning: a Probabilistic Perspective: https://probml.github.io/pml-book/book0.html

Deep learning books:

Deep Learning for Coders: https://github.com/fastai/fastbook

Hands-On Machine Learning: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/

Deep Learning with Python: https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438

More books here: https://tutobase.com/t/MachineLearning?tag=book

1 point

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19th Aug 2021

Machine learning books:

- An Introduction to Statistical Learning: https://www.statlearning.com/
- Pattern Recognition and Machine Learning: https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738
- Machine Learning: a Probabilistic Perspective: https://probml.github.io/pml-book/book0.html

Deep learning books:

- Deep Learning for Coders: https://github.com/fastai/fastbook
- Hands-On Machine Learning: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/
- Deep Learning with Python: https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438

1 point

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9th May 2021

Most classic ML books don't have code (maybe some pseudo code) but are math-heavy. You can check Bishop's book

1 point

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7th Dec 2020

Well for ML you have the holy trinity as:

This 3 book are increasingly Bayesian (you can read a comparison in the Murphy link)

Solution to different books here

For DL the classic Deep Learning book as introduction and this one for a formal mathematical approach for NN architectures

Finally for the implementation take a look at the site Papers with code

1 point

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1st May 2018

Would this book https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=mt_hardcover?_encoding=UTF8&me=

Be good for someone with no knowlege of machine learning that wants to learn.

1 point

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28th Dec 2017

> Pattern Recognition and Machine Learning

1 point

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4th May 2016

Start by Christopher Bishop's book titled 'Pattern recognition and machine learning' (http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738). It's a very good book that I would recommend to anyone new to the field. It is a bit old by now and won't go into deep learning much.

Something more recent and in video format is the excellent class on neural networks given by Hugo Larochelle (Neural networks class - Université de Sherbrooke: http://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH). This will broach the subject of deep learning and many of the more recent advances.

Hope this helps!

1 point

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10th Jun 2015

Getting started by clearing out some books, including text books, I've refused to let go of over the years. When it comes to books, what are the pros and cons of the different methods of selling?

- FBA - Easy, but less margin. Also, risk of merchandise sitting in their warehouse forever
- Amazon MF - More margin, but responsible for shipping items of various sizes and weights
- eBay - Even more margin, but less exposure than Amazon

Here are three books, all new or used - like new.

0387310738 - Good price, pretty high ranking

0072470461 - Great price, low ranking

0804831297 - Good ranking, not a huge amount.

Not looking for, "Do this, do that, go away," but some guidance that would help me choose the best method given different circumstances.

1 point

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9th Apr 2015

Listen to the Talking Machines podcast:

http://www.thetalkingmachines.com/

Start with an Introduction to ML text. I like Bishop:

1 point

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23rd Jan 2015

I'm a general engineer myself, with a side interest in computer science. Szeliski's book is probably the *big* one in the computer vision field. Another you might be interested in is Computer Vision by Linda Shapiro.

You may also be interested in machine learning in general, for which I can give you two books:

- Heavily Theoretical: Christopher Bishop - Pattern Recognition and Machine Learning (Very much research oriented. Assumes you're decent at calculus.)
- Heavily Practical: Toby Segaran - Programming Collective Intelligence (A shallow look at the most common practical algorithms straightforwardly applied to real life examples.)

I see you're interested in compilers. The Dragon book mainly focuses on parsing algorithms. I found learning about Forth implementations to be very instructive when learning about code generation. jonesforth is a good one if you understand x86 assembly.

1 point

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15th Sep 2012

You may want to study regression as a machine learning problem. I don't know your background, but if you are a mathematician, this approach probably isn't the best for you.

If you are only thinking of fitting polynomials, you only need a little trick to adapt linear regression.

1 point

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11th Nov 2010

0 points

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29th Oct 2018

Hi, i see you already have read some good books. I would also recommend Bishop's pattern recognition book for ML https://www.amazon.ca/Pattern-Recognition-Machine-Learning-Christopher/dp/0387310738

However, not sure whether they teach this in grad stat . Good Luck !

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