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UW Statistics Courses
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=== Machine Learning === Sometimes statistical inference is very hard. Prediction is often easier and sometimes predicting an outcome can be a useful contribution. Prediction and is also useful for constructing variables (e.g. content analysis). Supervised machine learning is essentially giving up on inference and focusing on prediction. "Unsupervised machine learning" (i.e. clustering) can be very useful for operationalization. If you do not have a computer science background, STAT 588 looks like a good place to get some quick and dirty machine learning. Fitting machine learning models can be difficult when you data is very big (as ours often is). STAT 548 is a good class to learn how to solve these problems. It mainly focuses on stochastic optimization. It isn't very difficult, but you will get more out of it if you are good at linear algebra and multivariate calculus. There are also 400 level introduction to machine learning classes in CSE and STAT, but STAT 588 looks better than either of these.
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