I’ve recently submitted the first tutor marked assignment of M347, my final course in the BSc Mathematics I’ve been studying with the Open University. The third unit of this course was long and quite a slog to go through. While I’ve been using many of these equations over the past few years, diving deep into the theory and derivation has been fascinating, although frustrating due to the lack of practical application. If you’ve read my other posts then you may recall how frustrated I was with group theory and the early parts of complex analysis, while the quantum world was far more engaging from the start1. As with all my maths studies, this exercise of filling in the gaps has revealed that there are far more things I didn’t know I didn’t know than things I knew I needed to know.
I studied maths to A-level, which was very light on statistics other than understanding the difference between mean, median and mode (!) and then did a degree in Biochemistry. The mathematics requirement for that course was some pretty basic statistics (sampling, correlation, hypothesis testing) and basic algebra. We touched on Fourier transforms to explain x-ray crystallography, but only as a description rather than an exercise. Everything I learned beyond that was self-taught as and when I needed it. There was a huge overhead on the mathematics I needed for my PhD, as I was modelling diffusion and electrical propagation through neurons as well as implementing abstract neurons for machine learning systems2. While this was all before StackOverflow, and even Google, existed, the same methodology applied: I need to do X, look up how to do something similar and adapt3. I’ve been using many of the techniques covered in M347 for years4, but the richness of a fully rounded course is broadening my toolkit beyond that day to day use and I’ve no doubt that some of this will have practical applications I hadn’t previously considered.
I’ve found M347 difficult to settle into, although have been working hard to ensure I don’t fall behind despite other commitments in my life5. The books themselves have humorous asides that are supposed to make the topics less of a slog, but come across as irritating interruptions. While I think they would work well in a lecture, in written form they are just annoying.
My other gripe is that the books are printed in greyscale and yet refer to diagrams that are in colour. My study patterns mean that I have the books and my surface or a notebook, but 99% of the time do not have an internet connection so rely heavily on the books and printouts6. For now, nothing has been critical, but the books would benefit from a static image of some of the interactive material even if they still direct you to the online version. That said, the actual topic is great – there is an immense beauty in mathematics and the statistical relationships that exist are no exception. I’d recommend anyone looking to get into machine learning to pick up this course or something similar.
- Maybe I should have taken the Physics course 😉 ↩
- This was actually a lot of fun and something I’ll write up as a post some day. Sadly my reflections on my viva are lost due to a website deletion. ↩
- Just in this case looking it up meant asking around the lab and then a trip to the library! ↩
- If you’ve been to any of my talks in 2019 you’ll know how much I push learning statistics as part of getting started in Machine Learning! ↩
- Although at the time of writing I am now a week behind based on the study planner provided. ↩
- I really try to avoid printouts where I can. While I could also download the PDFs, I use my surface to take notes so I just don’t have enough screen to make this practical. ↩