Updated in mid-July 2020. This article is the looks to summarise the learnings from a semester studying Machine Learning. I reflect on the course, discuss the three branches of machine learning, and kick off with an example. We look at a 'decision tree' model on customer satisfaction data, and touch on the fundamental topic of overfitting and underfitting.
We can't help but try to predict the future, especially when it comes to the application of new technologies. Who knew before the fact how radically our lives would transform due to the internet, computers, electricity and the industrial revolution? It seems the most recent development is the rise of AI and machine learning. Will this lead to a future like in Terminator and WALL-E, or something benign? We read about impressive advances such as game grandmasters beaten by AI, self-driving cars and using Alexa in the home. Naturally one might ask when we will have an AI that can beat chess grandmasters AND cook my pancakes? Most AI technology is quite specialised, and what we're referring to here is also known as 'General AI'. Unfortunately, this seems to be a long way off, but we can achieve a lot with focused effort in a single domain.
We've compiled a list of occassions where our analysis tools have behaved unexpectedly. Avoid the blood, sweat, tears and swearing by learning from our experience. We mainly look at Excel, but many of the problems turn out to be deep rooted in computer science and the way our machines handle and process data.