DNSC 6279 ("Data Mining") provides exposure to various data preprocessing, statistics, and machine learning techniques that can be used both to discover relationships in large data sets and to build predictive models. Techniques covered will include basic and analytical data preprocessing, regression models, decision trees, neural networks, clustering, association analysis, and basic text mining. Techniques will be presented in the context of data driven organizational decision making using statistical and machine learning approaches. DNSC 6290 ("Machine Learning") provides a follow up course to DNSC 6279 that will expand on both the theoretical and practical aspects of subjects covered in the pre-requisite course while optionally introducing new materials. Techniques covered may include feature engineering, penalized regression, neural networks and deep learning, ensemble models including stacked generalization and super learner approaches, matrix factorization, model validation, and model interpretation. Classes will be taught as workshops where groups of students will apply lecture materials to the ongoing Kaggle Advanced Regression and Digit Recognizer contests.