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Machine Learning & Data Mining I (NCHNAP563)

15 Credits

This course introduces the learner to three of the most well used machine learning techniques for data mining and predictive modelling: regression, decision trees, clustering and principal component analysis (PCA). Learners will explore the difference between supervised, unsupervised and machine learning, and study how to build and analyse robust predictive models using tools such as Python and R. It uses tools and libraries to analyze data sets, build predictive models, and evaluate the fit of the models. Covers common learning algorithms, including dimensionality reduction, classification, principal-component analysis, k-NN, k-means clustering, gradient descent, regression, logistic regression, regularization, multiclass data and algorithms, boosting, and decision trees. This course will also examine data bias and accurate model building: assessing the appropriateness of training and test sets, evaluation and deployment.