I526: Applied Machine Learning
Introduction to linear regression (and multivariate linear regression) and practical aspects of implementation. Logistic Regression and regularization. Decision trees and pruning, implementation of decision trees. Support vector machines and making them work in practice. Boosting - implementing different boosting methods with decision trees. Using the algorithms for several tasks - how to set up the problem, debug, select features and develop the learning algorithm. Unsupervised learning - k-means, PCA, hierarchical clustering. Implementing the clustering algorithms. Parallelizing the learning algorithms. Applications. Choosing from multiple algorithms - What will work?
Year(s) Offered: _2018
Class time: : ARR Online
Instructor: James Shanahan - email@example.com
Formal Computing Lab?: No
Software Used: MatLab, Python, C/C++
Comments: This class requires the permission of the instructor This is a 100% online class taught by IU Bloomington. No on-campus class meetings are required. A distance education fee may apply; check your campus bursar website for more information