|ML Tutorial Day: Machine Learning for Everyday Programming
|Track / Format:
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|Deep learning is a very popular topic today, and rightfully so, as it expanded the horizons of applicability of machine learning. But "shallow" models are often more interpretable and can lead to deeper insights and faster code. In general they are preferable over black box solutions for the problems that they can solve.This talk aims to introduce the topic of machine learning as a tool for "everyday" programming, as a methodology of data-oriented problem solving. The talk will cover techniques like multidimensional data visualization, dimensionality reduction, nonlinear regression and symbolic regression. The presentation will show some examples from concrete rendering problems, but this talk will not be aimed at rendering engineers. Lastly, the talk will show that this methodology of iterative data exploration and modeling is useful to gain insights on the problem domain that often can lead even to solutions that do not require a learned model.