WEEK1 — Case-study: exploring the possibilities of explaining Machine Learning visually
In order to explore the possibilities to explain complex concepts like Machine Learning, I really spent a lot of time (at least 4 hours! )reading the two visual articles. However, for a person who’s not a totally new learner of this concept, I really don’t have the memory that’s more vivid or the understanding that’s deeper about Machine Learning after studying it for a serious amount of time! And our professor who’s specialized in Machine Learning really doesn’t think it is the material that’s suitable for the public(“I think my examples of ‘cats’ and ‘dogs’ are better!” she said).
So, why?
What exactly are the swamps in this piece that makes people have a hard time to understand things?
In the first glance, it gives readers a kind of poetic feeling. Literally, it is a kind of ART. (It’s approved by all of its readers.)
- However, the main goal of the transitions between the charts driven by scrolling is to make all the sections into a completed experience, and that’s all. It doesn’t really serve for the explaining part. (except for the part about decision tree)
- And the second reason I think is simply that all the charts are still too abstractive and also too small especially when they aline with each other for further analysis.
(The authors of this case actually are really ambitious, and this kind of skill for data visualization just belongs to a few designers.)
Thus, if it was me, how exactly can I explain this concept? To make some metaphors? Even though this topic already out of my picture now (in terms of being the topic of my project this semester), it still remains an interesting question that’s worth to explore.
