One habit of mine is listening to the same songs so much they become less enjoyable, in spite of a world of other related music out there to enjoy as well. This problem leads to the project’s first goal; mapping a music collection so a recommendation system can be built from it, which is is complemented by a second goal; producing visual media using album artwork in the collection. The minimum viable product is an application to recommend music while involving generative imagery in the process.…
Week Five: “Choosing a supervised learning algorithm” decision tree (forum link) Arduino circuit photo and code (below) Paper questionnaire (handed in) Reflection: I found making a decision tree of common supervised machine learning algorithms not entirely straight-forward. The no free lunch theorem (Wikipedia link) states that algorithms can never be optimal for all problems. And the number of exceptions in the decision tree I made point to the same conclusion.…
Week Four: Q&A question on learn.gold (forum link) Online quiz on learn.gold Wekinator Assignment 4 on learn.gold (and here) Reflection: An interesting question asks what classes k-nearest neighbors algorithm as ‘machine learning’. It can be argued that it is so simple in comparison to neural networks and other machine learning algorithms that it just doesn’t fit. There is no training involved which is another way it stands out compared to most other solutions.…
Week Two: Online quiz on learn.gold Wekinator Assignment 2 group work on learn.gold openFrameworks app that responds to Wekinator classifier on learn.gold Code for the app demonstrated below: link Reflection: The third task this week; to create a piece of code that responds to a classifier, was surprising in several ways. With it being an early experiment, I made a simple program to demonstrate classification: clicking the mouse sends two features (current X and Y position within the 2-dimensional input space) to Wekinator.…
Week One: Online quiz on learn.gold Wekinator Assignment 1 on learn.gold Supervised Learning Ideas (forum link) Reflection: The aim of machine learning (ML) is to process an input in order to reach an output, without actually programming the decision-making stage (or the model in the pipeline) explicitly. My initial thoughts about this generalised ML schematic were that it seems extremely broad. Indeed, the input can be virtually any type of data; from single values to video streams.…