08 Feb : understanding a ML project : what are the good questions? • 20 Feb and following : ◦ more technical sessions ◦ optional “homework” between sessions (small projects) ◦ current plan : 7 technical sessions ◦ goal : be able to work on ML projects autonomously
are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.
the same question: how do we learn from data? Statistics Machine Learning Estimation Learning Classifier Hypothesis Data Point Example/ Instance Regression Supervised Learning Classification Supervised Learning Covariate Feature Response Label
on data • system is both the algorithm and the data • only as good as your data • starts with a hypothesis about how we can represent the data (for linear regression : a straight line) • can deal poorly with outliers • lots of calculation to learn, but very fast to apply (can run on mobile)
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