user7687: I am very interested in how AlphaGo resp. AlphaZero works. It seems to me, the related Google Papers are very dense and not easy to read.
Is there any textbook or lecture that explains on a technical level how it all works? I.e. starting with neural networks/deep learning, Monte Carlo Tree Search until reaching at AlphaGo/AlphaZero?
I have a master in computer science, but I never worked with artificial intelligence/machine learning.
Right now it looks to me that all the related information are scattered around, and I don't have time to listen to (online) lectures about machine learning that contain maybe only 10% on what is relevant to understand AlphaGo...
Can someone point me in the right direction?

user8844: This presentation summarizes the journey to AlphaGo
This is a presentation of the underlying theory
This is the book
Plus, I guess, anything on deep convolutional neural networks.

user15467: I don't have enough reputation to comment, but AlphaGo Zero Explained In One Diagram is pretty good.
I also really like this tutorial.
Note that the first link doesn't describe when to create (expand) nodes. That part can be a little confusing. This link may help.

user825: EDIT
@unutbu's link in the comment is a good introductory read.

Solid understanding for AlphaZero most likely require a quantitative degree (PhD?). Are you asking for a crash course in AlphaZero?
Please note unless you invest significant amount of your time, nothing I say will work. There is no book that can possibly cover everything. You will need to work very hard.
Let's give a try. Crash course for AlphaZero.
1. Basic understanding in machine learning
Countless number of books. But if you don't have time, I'd recommend Professor Andrew Ng's machine learning courses on Coursera. 
Your keywords: training sets, test sets, stochastic gradient descent, learning rate, GPU, cost function, cross entropy.
2. Deep neutral networks
You need to understand about neural networks. I'd recommend Professor Ian Goodfellow's deep learning book if you're serious. If you don't have time, please  again follow Professor Andrew Ng 's online course on Coursera. You don't have to go though all chapters (but you should!).
YouTube has many quick introduction to neural networks, try them.
Your keywords: neuron, layers, weights, bias, mini-batch, activation.
3. Monte Carlo Tree Search
You should understand what Monte Carlo is. Books about Monte Carlo is everywhere on Amazon. Read the wikipedia about MCTS if you don't have time.
Your keywords: sampling, expansion, simulation, rollouts, backpropagation.
3. Reinforcement learning

https://www.quora.com/What-is-the-REINFORCE-algorithm
http://www.scholarpedia.org/article/Policy_gradient_methods

Your keywords: policy gradient, gradient descent, learning rate
5. Chess board representation
The paper itself is simplest. The model encodes the board state (e.g. pieces) as a series of one-hot encoding binary values.
6. MCTS vs alpha-beta


7687: <p>I am very interested in how AlphaGo resp. AlphaZero works. It seems to me, the related Google Papers are very dense and not easy to read.</p>

<p>Is there any textbook or lecture that explains on a technical level how it all works? I.e. starting with neural networks/deep learning, Monte Carlo Tree Search until reaching at AlphaGo/AlphaZero?</p>

<p>I have a master in computer science, but I never worked with artificial intelligence/machine learning.</p>

<p>Right now it looks to me that all the related information are scattered around, and I don't have time to listen to (online) lectures about machine learning that contain maybe only 10% on what is relevant to understand AlphaGo...</p>

<p>Can someone point me in the right direction?</p>
 8844: <p><a href="https://www.youtube.com/watch?v=Bm7zah_LrmE" rel="nofollow noreferrer">This presentation summarizes the journey to AlphaGo</a></p>

<p><a href="https://www.youtube.com/watch?v=ggqnxyjaKe4&amp;list=PLe0J3_6vYq7vQjZQXLC0lauk9UFOehDXA" rel="nofollow noreferrer">This is a presentation of the underlying theory</a></p>

<p><a href="https://mitpress.mit.edu/books/reinforcement-learning" rel="nofollow noreferrer">This is the book</a></p>

<p>Plus, I guess, anything on deep convolutional neural networks.</p>
 15467: <p>I don't have enough reputation to comment, but <a href="https://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0" rel="nofollow noreferrer">AlphaGo Zero Explained In One Diagram</a> is pretty good.</p>

<p>I also really like <a href="http://web.stanford.edu/~surag/posts/alphazero.html" rel="nofollow noreferrer">this</a> tutorial.</p>

<p>Note that the first link doesn't describe when to create (expand) nodes. That part can be a little confusing. <a href="http://ml4dummies.blogspot.com/2018/01/some-confusion-i-encountered-while.html" rel="nofollow noreferrer">This</a> link may help.</p>
 825: <p><strong>EDIT</strong></p>

<p>@unutbu's link in the comment is a good introductory read.</p>

<hr>

<p>Solid understanding for AlphaZero most likely require a quantitative degree (PhD?). Are you asking for a crash course in AlphaZero?</p>

<p>Please note unless you invest <strong>significant</strong> amount of your time, nothing I say will work. There is no book that can possibly cover everything. You will need to work very hard.</p>

<p>Let's give a try. Crash course for AlphaZero.</p>

<p><strong>1.</strong> Basic understanding in machine learning</p>

<p>Countless number of books. But if you don't have time, I'd recommend Professor Andrew Ng's machine learning courses on Coursera. </p>

<p>Your keywords: <em>training sets, test sets, stochastic gradient descent, learning rate, GPU, cost function, cross entropy</em>.</p>

<p><strong>2.</strong> Deep neutral networks</p>

<p>You need to understand about neural networks. I'd recommend Professor Ian Goodfellow's deep learning book if you're serious. If you don't have time, please  again follow Professor Andrew Ng 's online course on Coursera. You don't have to go though all chapters (but you should!).</p>

<p>YouTube has many quick introduction to neural networks, try them.</p>

<p>Your keywords: <em>neuron, layers, weights, bias, mini-batch, activation</em>.</p>

<p><strong>3.</strong> Monte Carlo Tree Search</p>

<p>You should understand what Monte Carlo is. Books about Monte Carlo is everywhere on Amazon. Read the <a href="https://en.wikipedia.org/wiki/Monte_Carlo_tree_search" rel="nofollow noreferrer">wikipedia</a> about MCTS if you don't have time.</p>

<p>Your keywords: <em>sampling, expansion, simulation, rollouts, backpropagation</em>.</p>

<p><strong>3.</strong> Reinforcement learning</p>

<ul>
<li><a href="https://www.quora.com/What-is-the-REINFORCE-algorithm" rel="nofollow noreferrer">https://www.quora.com/What-is-the-REINFORCE-algorithm</a></li>
<li><a href="http://www.scholarpedia.org/article/Policy_gradient_methods" rel="nofollow noreferrer">http://www.scholarpedia.org/article/Policy_gradient_methods</a></li>
</ul>

<p>Your keywords: <em>policy gradient, gradient descent, learning rate</em></p>

<p><strong>5.</strong> Chess board representation</p>

<p>The paper itself is simplest. The model encodes the board state (e.g. pieces) as a series of one-hot encoding binary values.</p>

<p><strong>6.</strong> MCTS vs alpha-beta</p>

<p><a href="https://i.stack.imgur.com/UgOts.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/UgOts.png" alt="enter image description here"></a></p>
