Machine learning is the intersection of statistics and computer science. This class teaches key ML models (e.g., neural networks, support vector machines, hidden Markov) as well as fundamental concepts (e.g., feature selection, cross-validation and over-fitting).
In addition, coding practice is a core component of the class. Students produce weekly machine learning algorithms to make sense of a wide range of data, including images, time series, and numerical data.
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