"Machine Learning" sounds like miracles and wonders, like robots behaving like cute little kids maybe ... but it is not (yet) like this. ML is a collection of very powerful algorithms and algorithm design methods which is firmly rooted in probability theory, statistics, linear algebra and some calculus. ML is one of the most mathy areas of Computer Science. The input to an ML algorithm is (large quantities of) data - for example images, video, geo-surveillance data, scientific measurements, financial records, web-crawling harvests. The ML algorithm tries to find regularities (structure, redundancies, symmetries...) in the data and use these to create a formal representation of the data (a model) in a compressed format which should be much more compact than the original data. We may be talking about real large items here: current state-of-the-art ML algorithms may be fed with many billions of data points and "compact" them into a representation with millions of parameters. After having learnt a model from the data, the model can be exploited for a variety of purposes, for instance data interpolation or prediction, pattern classification, control, or pattern generation.
If you are contemplating choosing ML for your Guided Research thesis, the first thing for you is to find out whether you really like this kind of stuff and whether it is accessible to you. You get a fair impression of basic themes of ML and of the "flavor" of this field if you read Chapter 2 in my ML lecture notes. Yes, please do read that.
The next thing is to get an idea of the specific research that is carried out in the MINDS group at Jacobs. Your thesis work will be closely connected to it. There are three main lines of research:
To get a clearer picture of the first kind of research (echo state networks), please read the following papers in the given order:
You might also wish to check out a (very good) BSc thesis written on an ESN theme (by Valentin Vasiliu, 2016).
If you feel you might become interested in the observable operator model work, read the first part in the 2-part paper
Note: OOM theses written in the MINDS group seem to be good for carving an academic carreer path: Cristian Danescu-Mizil (Master thesis 2007) is now an assistant professor at Cornell, Anca Dragan (BSc thesis 2009) is assistant professor at Berkeley, and Josip Djolonga (BSc thesis 2011) is a PhD student at ETH Zurich with a Google European Doctoral Fellowship. You can check out Josip's BSc thesis if you want to get an OOM feeling.
Finally, research on conceptors is young and there is not much literature yet. A short "teaser paper" (11 pages) is
So far there has only been a single conceptor-based BSc thesis, - a fine one -, by Alina Dima (2014).
If you are done with this reading (of either of the three directions), we can start talking business... I don't have ready-made thesis topics. Rather, I'll be sitting together with you and define a thesis topic which is tailored to your interests and background.