Introduces the fundamental techniques for machine learning and data science engineering. Discusses a variety of machine learning algorithms, along with examples of their implementation, evaluation, and best practices. Lays the foundation of how learning models are derived from complex data pipelines, both algorithmically and practically. Topics include supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks, deep learning) and unsupervised learning (clustering, dimensionality reduction, recommender systems). Based on numerous real-world case studies.
The ability to use python is part of the grade. Students must demonstrate ability to setup data for learning, train, test, and evaluation using either python or R, but all assignment examples and solutions will be presented in python. The assignments include paper exercises designed to reinforce conceptual understanding. Quizzes and exams. A term project is required. A portfolio blog is required.