Philosophy of Mind, Philosophy of Metaphysics, Philosophy of Biology, and Symbolic Logic are all common Philosophy courses that have strong connections to CS, m

Philosophy of Mind, Philosophy of Metaphysics, Philosophy of Biology, and Symbolic Logic are all common Philosophy courses that have strong connections to CS, machine learning, and the biological models often behind (or inspiring at least) ML. Lower level Philosophy courses typically focus on the historical philosophy that most people think of as the entire discipline. It has been said that all philosophical debates go back to Plato or Aristotle, but this is hardly the case in these disciplines. Philosophy of Mind probably has the closest connection to ML in particular though, as most of the course I took anyway was devoted to Turing Machines, neural networks, etc. As far as general CS goes, Symbolic Logic is the most essential. Most CS programs cover this stuff, but subjects that might get a 1 week treatment in a typical Discrete Mathematics course would get a full semester in a typical Philosophy program. As far as general scientific thought and skepticism go, Philosophy of Science, Metaphysics, and Biology are key. If you want to dip your toes in this sort of thing, check out Francis Bacon and the falsification principle, along with Thomas Kuhn and his theory of scientific revolutions and paradigms. One last note: most people think about philosophy in terms of broad prescriptive pronouncements. Think Sartre, Plato, Nietzsche, Aquinas, Kant, etc. Most contemporary philosophers are more concerned with specific analysis of claims made by a paradigm, in which they explore whether or not these claims a) mean anything b) are internally consistent and c) lead to further implications. Coming out and making some completely new claim is exceedingly rare. An example of this (a bit contrived) in the context of ML and Biology would go something like: 1) Computer Scientists claim that neural network algorithms mimic biological brains. 2) What do we actually know about how biological neural networks work? 3) How do neural network algorithms typically mimic this? 4) Based on the current research, it turns out that neural networks in algorithms differ from their biological counterparts in X, Y, and Z ways. 5) Based on the efficacy these algorithms, how can we explain their ability to "walk like a duck and quack like a duck" without actually being ducks? 6) Assuming a reasonable advancement of this technology, is it reasonable to believe that we may reach a point when X, Y, and Z might be addressed? TLDR: if you got bored reading what I wrote above, you will probably abhor the ridiculous density and verbosity of most contemporary philosophy. Hey, I tried :)