If one has to teach a 15-week course titled "Artificial Intelligence and Machine Learning" to the postgraduate students of Electronic Engineering, what topics should one teach? Assume the students have not learned any dedicated course on AI or Machine Learning before.

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    $\begingroup$ What is your own expertise here? $\endgroup$
    – Buffy
    Mar 17, 2020 at 16:09
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    $\begingroup$ Hi MMKhan! I reopened your question upon closer inspection because you are asking about a very different level of student. However, you may still find the answers at cseducators.stackexchange.com/questions/5080/… to be useful. Take a look! $\endgroup$
    – Ben I.
    Mar 17, 2020 at 16:21
  • $\begingroup$ @Buffy I have expertise in statistical classification and had a course on computational intelligence before. $\endgroup$
    – MM Khan
    Mar 18, 2020 at 3:30
  • $\begingroup$ Hi @mm-khan, did you try to take inspiration from the programs of the countless courses available online? And on top of that, do you know whether the students are fluent at at least one programming language? Python could be a good language for ML beginners since it has lots of libraries on the matter, sci-kit learn the first that come to my mind. $\endgroup$
    – danidemi
    May 1, 2020 at 0:57
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    $\begingroup$ To quote Steven Covey, "Begin with the End in Mind". I've always found it useful to start with desired course outcomes and then work backwards. There are lots of ways to teach a course like this, all equally valid, depending on what your goals are. $\endgroup$
    – lfalin
    Jun 5, 2020 at 18:27

1 Answer 1


Here's my proposal.

  1. A little bit of math to "refresh" the concepts needed in the next module.
  2. A little bit of statistics. This is my opinion can be used to analyze data sets from a statistical point of view, useful for some machine learning trained algorithms.
  3. The basics of one programming language you want to use across the course. Not knowing all the details I would say Python because there are a lot of libraries for ML available.
  4. Main neural networks and examples.
  5. Expert systems. Not very much hype about these currently, but in my opinion a good option.
  6. Machine learning algorithms and examples.

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