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I took this course last term after reading an introductory book on machine learning and skimming through Andrew Ng's CS 229 lecture notes. I thought this class was particularly excellent at emphasizing the theoretical aspects of machine learning, as well as emphasizing some underlying themes (like avoiding overfitting with regularization and cross validation). The class didn't cover as many models and algorithms as many of the other ML classes, but I've found those relatively easy to learn with the intuition this course gave me.



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