<p>This recent <a href="https://www.youtube.com/watch?v=jAzC1fVksng" rel="nofollow">KISSCaltech lecture</a> by Prof. Rob Fergus at the NYU might be of interest. <em>"An Overview of Machine Learning Approaches: Applications to Exoplanet Detection"</em>.</p>

CC BY-SA 3.0
2016-10-10T16:42:39.930Z
What about books? I would like to have some solid literature to come back to while I do my research...
Marek Sagan
CC BY-SA 3.0
2016-10-10T16:51:31.450Z
Would you mind expanding on this, or at least give a description about why it's good? Right now this seems like a link-only answer.
Sir Cumference
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2016-10-10T19:36:57.407Z
@SirCumference Well, I first posted it as a comment. But since the question asks for "other resources", I thought that this current overview lecture of machine learning applied to exoplanets, from a renowned source, would be satisfactory as a partial answer. Since it might not yet be the content of text books. It's as valid as the question is formulated. It
LocalFluff
CC BY-SA 3.0
2016-10-10T20:34:20.710Z
@LocalFluff I'd say the answer still should have an overview on what the link is, why it's good, or at least some kind of elaboration.
Sir Cumference
CC BY-SA 3.0
2016-10-11T12:30:53.950Z
This is an incredibly useful answer, thanks for that Fluffster.  (I'm not knocking SirCumf, by the way; my comment is coincidental.)
Fattie
CC BY-SA 3.0
2016-10-10T16:18:18.377Z
2016-10-10T16:18:18.377Z
LocalFluff
<p>I am a Computer Engineering major and would like to learn more about machine learning methods in Astronomy. What books or other resources could I use?</p>

CC BY-SA 3.0
2016-10-10T16:07:45.383Z
There may be some information on [Galaxy Zoo](https://www.galaxyzoo.org) - if I remember right they use volunteers online to identify galaxies, and the long term goal is to use the data to train search algorithms. [This blog entry may be useful](https://blog.galaxyzoo.org/2015/03/31/new-paper-galaxy-zoo-and-machine-learning).
Andy
CC BY-SA 3.0
2016-10-11T11:36:34.117Z
Isn't asking for resources OT in this SE topic?
Carl Witthoft
CC BY-SA 3.0
2016-10-12T14:55:43.480Z
Voted close as this is very opinion based. The question is not related to facts of astronomy and astrophysics, just a request for resources. Personally I would suggest Google.
FJC
CC BY-SA 3.0
2016-10-12T17:11:15.240Z
@FJC Data mining & machine learning are the methods of contemporary astronomy... I do not see how this is not related to the question.
Marek Sagan
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2016-10-13T11:10:27.513Z
@MarekSagan To clarify: you are not asking a question about methods of data mining or a specific use of machine learning, you are asking for book recommendations. Any answer will be one persons opinion on what is a good book/website resource, not a scientific researched fact or theory. Google gives you a lot of resources (books/websites/papers) to get you started, and if you can't understand a specific thing you can come back here to ask questions, but this isn't the correct forum for your question at this stage.
FJC
CC BY-SA 4.0
2020-04-07T15:31:44.560Z
There is this coursera MOOC https://www.coursera.org/learn/data-driven-astronomy/home/welcome
usernumber
<observational-astronomy><astrophysics><radio-astronomy><data-analysis>
Astronomy & machine learning. Where to start?
Marek Sagan
# Astronomy & machine learning. Where to start?

(Asked by: username_0 on 2016-10-10)

I am a Computer Engineering major and would like to learn more about machine learning methods in Astronomy. What books or other resources could I use?

**Tags:** observational-astronomy, astrophysics, radio-astronomy, data-analysis
> **Comments:**
> **username_1**: There may be some information on [Galaxy Zoo](https://www.galaxyzoo.org) - if I remember right they use volunteers online to identify galaxies, and the long term goal is to use the data to train search algorithms. [This blog entry may be useful](https://blog.galaxyzoo.org/2015/03/31/new-paper-galaxy-zoo-and-machine-learning).  
> **username_2**: Isn't asking for resources OT in this SE topic?  
> **username_3**: Voted close as this is very opinion based. The question is not related to facts of astronomy and astrophysics, just a request for resources. Personally I would suggest Google.  
> **username_0**: @FJC Data mining & machine learning are the methods of contemporary astronomy... I do not see how this is not related to the question.  
> **username_3**: @MarekSagan To clarify: you are not asking a question about methods of data mining or a specific use of machine learning, you are asking for book recommendations. Any answer will be one persons opinion on what is a good book/website resource, not a scientific researched fact or theory. Google gives you a lot of resources (books/websites/papers) to get you started, and if you can't understand a specific thing you can come back here to ask questions, but this isn't the correct forum for your question at this stage.  
> **username_4**: There is this coursera MOOC https://www.coursera.org/learn/data-driven-astronomy/home/welcome  

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## 1 answers

### Answer by username_5 on 2016-10-10. Score: 4 | Accepted answer

This recent [KISSCaltech lecture](https://www.youtube.com/watch?v=jAzC1fVksng) by Prof. Rob Fergus at the NYU might be of interest. *"An Overview of Machine Learning Approaches: Applications to Exoplanet Detection"*.

> **Comments:**
> **username_0**: What about books? I would like to have some solid literature to come back to while I do my research...  
> **username_6**: Would you mind expanding on this, or at least give a description about why it's good? Right now this seems like a link-only answer.  
> **username_5**: @SirCumference Well, I first posted it as a comment. But since the question asks for "other resources", I thought that this current overview lecture of machine learning applied to exoplanets, from a renowned source, would be satisfactory as a partial answer. Since it might not yet be the content of text books. It's as valid as the question is formulated. It  
> **username_6**: @LocalFluff I'd say the answer still should have an overview on what the link is, why it's good, or at least some kind of elaboration.  
> **username_7**: This is an incredibly useful answer, thanks for that Fluffster.  (I'm not knocking SirCumf, by the way; my comment is coincidental.)  

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