Students

In order to show students what they can achieve in their respective subjects with data science methodology, a survey was carried out among the current students. Here questions were asked about the courses and the content conveyed. One of our goals for the website is to show how students at the University of Regensburg think about artificial intelligence, machine learning and big data. The following aspects were discussed in the interview:

Mentioned courses:

  • Information science
  • Media informatics
  • Public history
  • Digital humanities
  • Media studies

With almost 60%, a little more than half are Bachelor students. Approx. 40% are master’s students.

Around half of the respondents stated that they were in their 4th semester. Almost a fifth was in the 3rd semester and the rest was spread over the 2nd and 6th semester. So most of the respondents were in the middle of their studies.

Slightly more than half of the respondents said they were male. The large number of women is surprising.

A third of the respondents were between 18 and 20, 21 and 23 or 24 and 26. Only one respondent said he was over 27 or 30.

60% stated that the subject area played a large to very large role in their degree program. For the remaining 30%, these issues play a rather minor role.

Almost all respondents stated that they had already attended events on the topics mentioned. Mentioned events / topics:

  • Machine learning
  • Web and Data Science
  • Human-machine interaction
  • Acquisition, preprocessing and analysis of sensor data
  • Information systems
  • Digitization and digital society
  • Information retrieval
  • Natural Language Engineering
  • Genomic and Bioinformatics
  • Advanced Topics of Optimization
  • Algorithms
  • Advanced Topics in Information Retrieval
  • Digital humanities
  • Representation of certain and uncertain knowledge
  • Computation Intelligence
  • Information linguistics

More than three quarters of the respondents attended the events because they were a compulsory part of their studies.

    • “Especially machine learning I and II in any case. Conveys all the important basic knowledge in the field of artificial intelligence, you learn the underlying mathematics and how to apply the methods”
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    • “In theory, I would recommend choosing the main focus of the mandatory events, but it is then also helpful to take courses from the IW or something else in advance that introduces the topic and that you try something more general, since the first one larger project in the direction of machine learning has now been implemented without much preparatory work and the system to be built was already a more advanced one, for which there were hardly any good tutorials and you felt a bit lost. “
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    • “Yes, definitely. All the other courses go far too little into detail and convey machine learning content anything but ‘hands-on'”
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    • “I would recommend DDG I, as it provides a good rough overview of what machine learning, AI, BigData or similar play a role in everyday life and what makes these things possible”
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    • “Good overview”
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    • “Would rather not recommend because there is hardly any concrete application (e.g. with examples, sample solutions, etc.)”
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    • “Yes, better understanding of machine learning in detail and in business / society”
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    • “Yes, because the topics are interesting”
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    • “Yes, because you get a good overview of the technologies mentioned without too many technical details”
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    • “Yes, I find the topic very interesting”
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    • “Natural Language Engineering only deals with AI, machine learning only incidentally – these topics are repeatedly addressed, but mostly only of a theoretical and superficial nature. But still an extremely valuable course, because it provides important basic requirements for these technologies.”
    • “At first it is difficult to fully understand the complex mathematical concepts, but over time you notice similarities, which makes it easier to deal with them”
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    • “It’s rather difficult to deal with, at least in the courses in which it came up, as there was no corresponding course in which you had to practically implement exactly what was ultimately the subject of one of the later courses and everything had to be worked out, only given There were hardly any tutorials or clues for the particular machine learning area, so it was very difficult to get into the topic from scratch. “
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    • “Light”
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    • “Rather easy”
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    • “Partly in part, to understand the basic principle is actually easy for me, when it goes deeper, I sometimes find it difficult”
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    • “Rather difficult. I like to understand things from the ground up, and with machine learning it is often difficult to understand exactly how the result comes about.”
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    • “The topics are very mathematical, require a lot of training and even more time to analyze the results. Since this field is very extensive, good basic knowledge is absolutely important.”
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    • “Difficult, very complex topic. In particular, understanding machine learning itself, as it is based on mathematical principles and complex algorithms are very abstract.”
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    • “Rather difficult”
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    • “At the beginning it seems rather difficult, especially with the math that is hidden behind it. But if you use Tensorflow / Keras as a black box, for example, it is manageable.”
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    • “is very complex”
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    • “Partly complicated, but interesting”
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    • “It depends on how the professor explains the topic, sometimes easy and clear examples are missing.”
    • “Definitely offer more events yourself at the chair (currently mainly in the field of physics); make voluntary offers from other faculties more visible and easier to bring in”
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    • “It may be useful to attend a basic course for the required mathematical knowledge before the machine learning event.”
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    • “More events in which the topics are taken up even more”
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    • “I can’t estimate yet, because the topic has only been dealt with incidentally, but I would say that several events are good for this. Just with regard to after university: the more you have dealt with the topic, the easier it is a professional entry. And at the moment this is an enormously rapidly growing research area. So the material does not run out. “
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    • “An introduction to machine learning itself would be helpful because, as far as I know, AI is already offered as an introduction in a certain way, but when I took the courses at the time, they had been restructured and the lectures were about the topics , but in practice you haven’t done that much. It would also be helpful to focus on a possibility of machine learning integration, e.g. a course in which you build such a system with Python as a basis and then later possibly also in Tensorflow or PyTorch or introduce other possibilities. “
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    • “A lot more events are necessary! We often talk about AI but it feels like nobody teaches you. Something like Jupyter Notebook seems to be alien to most professors.”
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    • “more reflection on implications and benefits for realities of life”
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    • “Analogous to other events, I would have expected concrete exercises that go from easy to difficult and not a fat theory praise, probably a session to introduce software and then expect us to teach ourselves the rest.”
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    • “Practical examples to try out for yourself would certainly help with understanding”
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    • “There should be lectures on the basics of neural networks, especially how to implement simple examples yourself”
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    • “I think more events on these topics would make perfect sense”
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    • “Light and clear examples and yes more events would be good too”
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    • “In Computational Intelligence, an exercise unit where Tensorflor / Keras Deep Neural Networks are implemented would still be desirable.”
    • “I don’t think I would visit because I don’t want to go into computer science.”

    • “Higher Semester”

More than three quarters of those questioned were previously unfamiliar with the site.

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    • “Yes, because it is playing an increasingly important role in a wide variety of sectors. Almost every area nowadays uses machine learning or big data approaches in some form.”

    • “Yes, I think. It permeates everyday life for me, also from a student perspective: as I said, I had the feeling of having to apply machine learning skills even though nobody taught me. The quality of the projects also suffers as a result”

    • “Yes, because it is a very important topic for the future – everyone should have at least a little idea of ​​how their voice assistant works on a mobile phone or something similar”

    • “Yes, I think it is important because it is important that these terms are not just thrown around as catchphrases, but that we understand what is behind them and, if possible, also gain practical experience with them.”

    • “Yes, I think that sooner or later AI etc. will find its way into every area of ​​our life and therefore one should have at least a vague basic understanding of it.”

    • “It depends, I think that this should already be addressed in many areas, because it is ubiquitous in everyday life, even if you may not even notice it. Nevertheless, not all students need it for their later professional life.”

    • “This AI summer should be used to profit from the fruits of summer in the next AI winter.”

    • “Yes, because it is very present especially nowadays through all the advertising / algorithms”

    • “Yes, this is a future issue”

    • “I think it depends on what you are studying. In computer science courses, definitely.”

    • “It depends on what focus you have as a student, in other words, in which direction you would like to continue your education. The Master IW leaves some freedom that does not necessarily have to be filled with the field of AI, etc. Many students also choose IW, because they particularly value the mathematics and IT requirements that are not to be deepened. “

    • “Yes, because these topics are increasingly influencing our everyday lives (e.g. Cambridge Analytica, targeted advertising). In my opinion, it is important that students have a little insight into how these technologies work and are therefore more aware of how your data will be processed.”

    • “Yes, as it is becoming more and more important”

    • “In certain courses of study, it is definitely interesting to deal with the topics, precisely because students who otherwise use the Internet a lot are concerned with something now and in the future. You can also look at the topics from different points of view (ethically, what opportunities it brings, the dangers, the advantages, for whom it can still be useful) “

    • “Yes, as it will in all likelihood be a big part of our future”

    • “Yes, because I think it will be important in many professions”

    • “Yes, since that’s just the future.”

 

  • “I think I have already given my opinion on this in detail. The best AI / courses are made by other faculties (so far)”

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