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Machine Learning or CSBP 411

Output:
This course introduces the fundamental concepts of machine learning. Topics include extracting and identifying useful features that best represent the data. Pre-processing methods such as replacing missing entries, feature selection, discretization and popular supervised and unsupervised learning algorithms such as linear regression, decision trees, k-nearest neighbor, Bayesian learning, support vector machines, neural networks and k-means are also covered in the course. Topics related to evaluating what is learned include evaluation strategies, cross-validation, Leave-one-out, Bootstrap prediction probabilities. Applications covered in the course include text and web mining, document classification, bioinformatics. The course is accompanied by hands-on problem solving using some of the popular machine learning toolboxes and programming languages.