This course surveys the techniques central to the modern practice of extracting useful patterns and models from large bodies of data and the theory behind these techniques. Students will learn the purpose, power, and limitations of models, with concrete examples from business and science. Course subject matter may include classification and regression, supervised segmentation and decision trees, similarity/distance metrics and recommender systems, clustering and nearest neighbors, support vector machines, understanding and avoiding overfitting, natural language processing and sentiment analysis, machine learning, neural networks, and AI, and logistic regression.
Course Credits
3