One of my last post was " I don't do machine learning ". Now, I realized that I am actually doing it (more on this below). However what I was doing previously? I would say I was doing data learning . What is different in machine learning to methods? After reading a lot, I think that the differences are on the focus, a regression (substitute for other procedures that model something) can be used for both machine learning and data learning. When I do a linear regression in data learning I focus on which variables have more weight and improve the adjusted R 2 . I learn from the data what is happening . When I do machine learning I focus on which linear regression is better with the data I have (or with new data). I learn from the data what models are better. That model can be a simple regression or a complicated deep neural network. Both have the same data, but the goal is different. Of course, nowadays where it is not too computationally expensive we want both:
About bioinformatics, science and some personal achievements around bioinformatics