DCS635

DCS

635

Machine Learning

Machine Learning for Data Science course will cover basic machine learning techniques such as classification, regression, dimension reduction, and clustering. Through the introduction of basic ML techniques, students will learn numerous ML methodologies for both supervised and unsupervised learning. Topics include classification techniques, random forests, neural networks, and tree-based models. Dimension reduction techniques for visualization and data analysis will also be covered. This course will then extend to the application and theoretical foundation of deep learning algorithms. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras.

Graduate

School of Computing, Applied Science, & Engineering