<p>For some material more recent than Kearns and Vazirani, you could check out Rocco Servedio's lecture notes for <a href="http://www.cs.columbia.edu/~rocco/Teaching/S12/">Advanced Topics in Computational Learning Theory</a>, or the notes from <a href="http://www-stat.wharton.upenn.edu/~rakhlin/courses/stat928/stat928_notes.pdf">Sasha Rakhlin's class</a>.</p>

CC BY-SA 3.0
2012-07-08T02:06:05.590Z
The first option looks great, are Rakhlin's notes accessible to someone without a statistics background?
Artem Kaznatcheev
CC BY-SA 3.0
2012-07-08T11:50:10.143Z
Yes, they should be.
Aaron Roth
CC BY-SA 3.0
2012-07-08T02:00:43.533Z
2012-07-08T02:00:43.533Z
Aaron Roth
<p><a href="http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&amp;tid=7334">Kearns and Vazirani</a> is maybe a bit old, but good introduction.</p>

CC BY-SA 3.0
2012-07-08T01:12:15.063Z
thanks! Is there a good survey of post-1994 results to compliment this book?
Artem Kaznatcheev
CC BY-SA 3.0
2012-07-08T01:03:43.177Z
2012-07-08T01:03:43.177Z
Sasho Nikolov
<p>Recently I've been reading a decent number of CoLT papers. Although I don't struggle with the individual papers (at least not more than I usually struggle with other theory papers), I don't feel I have a good broad grasp of the field as a whole.</p>
<p><strong>Is there a standard text, surveys, or lecture notes for introducing CoLT at the graduate level?</strong></p>
<p>I have a basic Theory A background, but no specific knowledge of Machine Learning or Statistics. I am mostly interested in things like PAC-learning and learning automata, and less interested in things like Bayesian inference and VC theory.</p>
<h3>Related questions</h3>
<ul>
<li><a href="https://cstheory.stackexchange.com/q/9027/1037">Resource / book for recent advances in statistical learning theory</a></li>
</ul>

<reference-request><machine-learning><lg.learning><survey><books>
Introductory resources on Computational Learning Theory
Artem Kaznatcheev
# Introductory resources on Computational Learning Theory

(Asked by: username_0 on 2012-07-07)

Recently I've been reading a decent number of CoLT papers. Although I don't struggle with the individual papers (at least not more than I usually struggle with other theory papers), I don't feel I have a good broad grasp of the field as a whole.

**Is there a standard text, surveys, or lecture notes for introducing CoLT at the graduate level?**

I have a basic Theory A background, but no specific knowledge of Machine Learning or Statistics. I am mostly interested in things like PAC-learning and learning automata, and less interested in things like Bayesian inference and VC theory.

### Related questions

* [Resource / book for recent advances in statistical learning theory](https://cstheory.stackexchange.com/q/9027/1037)

**Tags:** reference-request, machine-learning, lg.learning, survey, books

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## 2 answers

### Answer by username_1 on 2012-07-08. Score: 7 | Accepted answer

For some material more recent than Kearns and Vazirani, you could check out Rocco Servedio's lecture notes for [Advanced Topics in Computational Learning Theory](http://www.cs.columbia.edu/~rocco/Teaching/S12/), or the notes from [Sasha Rakhlin's class](http://www-stat.wharton.upenn.edu/~rakhlin/courses/stat928/stat928_notes.pdf).

> **Comments:**
> **username_0**: The first option looks great, are Rakhlin's notes accessible to someone without a statistics background?  
> **username_1**: Yes, they should be.  

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### Answer by username_2 on 2012-07-08. Score: 7

[Kearns and Vazirani](http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=7334) is maybe a bit old, but good introduction.

> **Comments:**
> **username_0**: thanks! Is there a good survey of post-1994 results to compliment this book?  

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