One of the most rapid growing areas in AI today is Computer Vision. There are many practical needs where the results of your Master Thesis can be helpful. You can try research something like Emotion Detection, Eye-Tracking, etc.
An appropriate work for a MS in CS in any good University can highlight the current status of research on this field, compare different approaches and algorithms. As a practical part, it makes also a lot of fun when your program recognizes your mood properly :)
If you want to work more on non trivial datasets (not google size, but not trivial either and with real application), with an objective measure of success, why not working on the netflix challenge (the first one) ? You can get all the data for free, you have many papers on it, as well as pretty good way to compare your results vs other peoples (since everyone used exactly the same dataset, and it was not so easy to "cheat", contrary to what happens quite often in the academic literature). While not trivial in size, you can work on it with only one computer (assuming it is recent enough), and depending on the type of algorithms you are using, you can implement them in a language which is not C/C++, at least for prototyping (for example, I could get decent results doing things entirely in python).
Bonus point, it passes the "family" test: easy to tell your parents what you are working on, which is always a pain in my experience :)
Music-related tasks
A bit more original: something that is both cool, not trivial but not too complicated in data handling is anything around music, like music genre recognition (classical / electronic / jazz / etc...). You would need to know about signal processing as well, though - I would not advise it if you cannot get easy access to professors who know about the topic.
Since it has a business tie in - given some input set determine probable business fraud from the input (something the SEC seems challenged in doing). We now have several examples (Madoff and others). Or a system to estimate investment risk (there are lots of such systems apparently but were any accurate in the case of Lehman for example).
Many great answers posted already, but I wanted to add my 2 cents.There is one hot topic in which big companies all around are investing lots of resources into, and is still a very challenging topic with lots of potential: Automated detection of fake news.
This is even more relevant nowadays where most of us are connecting though social media and there's a huge crisis looming over.
Fake news, content removal, source reliability... The problem is huge and very exciting. It is as I said challenging as it can be seen from many perspectives (from analising images to detect fakes using adversarial netwotks to detecting fake written news based on text content (NLP) or using graph theory to find sources) and the possbilities for a research proyect are endless.
I suggest you read some general articles (e.g this or this) or have a look at research articles from the last couple of years (a quick google seach will throw you a lot of related stuff).
I wish I had the opportunity of starting over a project based on this topic. I think it's going to be of the upmost relevance in the next few years.
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当今人工智能发展最快的领域之一是计算机视觉。硕士论文的结果可以对许多实际需求有所帮助。你可以尝试研究情绪检测、眼球追踪等。
任何优秀大学的计算机科学硕士的合适工作都可以突出该领域的研究现状，比较不同的方法和算法。作为一个实用的部分，当你的程序正确识别你的心情时，它也会变得很有趣:)
One of the most rapid growing areas in AI today is Computer Vision. There are many practical needs where the results of your Master Thesis can be helpful. You can try research something like Emotion Detection, Eye-Tracking, etc.
An appropriate work for a MS in CS in any good University can highlight the current status of research on this field, compare different approaches and algorithms. As a practical part, it makes also a lot of fun when your program recognizes your mood properly :)
Netflix
如果您想更多地研究不平凡的数据集（不是谷歌大小，但也不是微不足道的，并且具有实际应用程序），并客观地衡量成功，为什么不研究 netflix 挑战（第一个）？您可以免费获得所有数据，您有很多论文，以及将您的结果与其他人进行比较的好方法（因为每个人都使用完全相同的数据集，并且“作弊”并不那么容易，相反学术文献中经常发生的事情）。虽然大小不小，但您只需一台计算机就可以处理它（假设它足够新），并且根据您使用的算法类型，您至少可以用非 C/C++ 的语言来实现它们用于原型设计（例如，我完全用 python 做事可以获得不错的结果）。
加分点，它通过了“家庭”测试：很容易告诉你的父母你在做什么，这在我的经验中一直是一个痛苦:)
与音乐相关的任务
更原始一点：一些东西围绕音乐的任何东西都很酷，不是微不足道的，但在数据处理方面也不太复杂，比如音乐流派识别（古典/电子/爵士乐/等...）。不过，您还需要了解信号处理 - 如果您无法轻松接触了解该主题的教授，我不会建议您这样做。
Netflix
If you want to work more on non trivial datasets (not google size, but not trivial either and with real application), with an objective measure of success, why not working on the netflix challenge (the first one) ? You can get all the data for free, you have many papers on it, as well as pretty good way to compare your results vs other peoples (since everyone used exactly the same dataset, and it was not so easy to "cheat", contrary to what happens quite often in the academic literature). While not trivial in size, you can work on it with only one computer (assuming it is recent enough), and depending on the type of algorithms you are using, you can implement them in a language which is not C/C++, at least for prototyping (for example, I could get decent results doing things entirely in python).
Bonus point, it passes the "family" test: easy to tell your parents what you are working on, which is always a pain in my experience :)
Music-related tasks
A bit more original: something that is both cool, not trivial but not too complicated in data handling is anything around music, like music genre recognition (classical / electronic / jazz / etc...). You would need to know about signal processing as well, though - I would not advise it if you cannot get easy access to professors who know about the topic.
我可以使用相同的 答案我在之前的类似问题上使用过：
Russ Greiner 为他的机器学习课程提供了一份很棒的项目主题列表，因此这是一个很好的起点。
GA 和 ANN 都是学习器/分类器。所以我问你一个问题，什么是值得学习的有趣“东西”？也许是：
天空才是极限，真的！
I can use the same answer I used on a previous, similar question:
Russ Greiner has a great list of project topics for his machine learning course, so that's a great place to start.
Both GAs and ANNs are learners/classifiers. So I ask you the question, what is an interesting "thing" to learn? Maybe it's:
The sky is the limit, really!
因为它有业务联系——给定一些输入集，从输入中确定可能的商业欺诈（美国证券交易委员会似乎在做这件事上面临挑战）。我们现在有几个例子（麦道夫和其他人）。或者一个估计投资风险的系统（显然有很多这样的系统，但在雷曼兄弟的例子中都是准确的）。
起点可能是 Chen 的书计算金融中的遗传算法和遗传编程 。
以下是 AAAI 向全国证券交易商协会颁发的奖项的文章，该系统的系统监控纳斯达克内幕交易。
Since it has a business tie in - given some input set determine probable business fraud from the input (something the SEC seems challenged in doing). We now have several examples (Madoff and others). Or a system to estimate investment risk (there are lots of such systems apparently but were any accurate in the case of Lehman for example).
A starting point might be the Chen book Genetic Algorithms and Genetic Programming in Computational Finance.
Here's an AAAI writeup of an award to the National Association of Securities Dealers for a system thatmonitors NASDAQ insider trading.
已经发布了很多很棒的答案，但我想加上我的 2 美分。有一个热门话题，周围的大公司都投入了大量资源，并且仍然是一个非常具有挑战性和潜力的话题：假新闻的自动检测。
如今，这一点更加重要，因为我们大多数人都通过社交媒体进行联系，并且一场巨大的危机迫在眉睫。
假新闻、内容删除、来源可靠性……问题巨大且非常令人兴奋。正如我所说，它具有挑战性，因为它可以从多个角度（从使用对抗网络分析图像来检测假货，到基于文本内容（NLP）检测假书面新闻或使用图论来查找来源）以及研究的可能性来看待项目是无止境的。
我建议您阅读一些一般性文章（例如 此或this）或者看看过去几年的研究文章（快速谷歌搜索会给你带来很多相关的东西）。
我希望我有机会重新开始一个基于这个主题的项目。我认为这将是未来几年最重要的事情。
Many great answers posted already, but I wanted to add my 2 cents.There is one hot topic in which big companies all around are investing lots of resources into, and is still a very challenging topic with lots of potential: Automated detection of fake news.
This is even more relevant nowadays where most of us are connecting though social media and there's a huge crisis looming over.
Fake news, content removal, source reliability... The problem is huge and very exciting. It is as I said challenging as it can be seen from many perspectives (from analising images to detect fakes using adversarial netwotks to detecting fake written news based on text content (NLP) or using graph theory to find sources) and the possbilities for a research proyect are endless.
I suggest you read some general articles (e.g this or this) or have a look at research articles from the last couple of years (a quick google seach will throw you a lot of related stuff).
I wish I had the opportunity of starting over a project based on this topic. I think it's going to be of the upmost relevance in the next few years.