ACS 3510 Machine Learning (DS 2.1)
This course explores the foundations of modern machine learning algorithms and their practical application to solving data science problems. The course covers supervised learning techniques including decision trees, naive Bayes, k-nearest neighbors, linear and logistic regression, support vector machines, and ensemble methods including random forests and gradient boosting. The course will also cover unsupervised learning techniques including k-means, principal components analysis and hierarchical clustering. Students learn to use industry-standard modern software libraries and tools to solve a variety of data science problems via an "end-to-end" approach, building pipelines to ingest, clean, preprocess and transform data sets, and train, evaluate and fine-tune models. Prerequisites:
ACS 2510 (DS 1.1)