SyllabusThe goal of this class is to provide a broad introduction to machine-learning. The topics covered in this class include some topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting and perceptrons, and topics in unsupervised learning, such as k-means, and hierarchical clustering. The topics covered in this class will be different from those covered in CSE 150. In addition to the actual algorithms, we will be focussing on the principles behind the algorithms in this class. TextbookThere is no required text for this course. Slides or notes will be posted on the class website. We recommend the following textbooks for optional reading.
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