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?
Semester(s) Offered: Fall
Instructor: Sriraam Natarajan
Other Contact(s): Sriraam Natarajan
Algebra Required?: Basic Algebra
Calculus Required?: Basic calculus
Day(s) per week offered: Two.
Formal Computing Lab?: No
Software Used: MatLab, Python, C/C++
How the software is used: Programming Homeworks
Data Analysis: Yes