ACS 3510 Machine Learning (DS 2.1)
This course explores the foundations of machine learning and how to apply those techniques to data science. Key concepts include comparing accuracy measures to evaluate the quality of models, features, and training methods and exploring supervised learning techniques including decision trees, naive Bayes, k-nearest neighbors, linear and logistic regression, support vector machines, and neural networks. The course will also cover unsupervised learning techniques including k-means and hierarchical clustering, and ensemble learning methods including random forests and adaptive boosting. Students learn the data science process and apply these techniques to train models on data sets using industry-standard modern software libraries and tools. Prerequisites:
ACS 2510