CS 559 Machine Learning: Fundamentals and Applications
Decision Theory
Explain Bayesian decision theory, the likelihood ratio, and minimum risk classification.
Maximum Likelihood Estimation
Implement Maximum Likelihood Estimation for Logistic Regression.
Dimensionality Reduction
Apply dimensionality reduction using Principal Component Analysis.
Linear Discriminant Functions
Implement classifiers using linear discriminant functions and Fisher’s Linear Discriminant Analysis.
Non-parametric Learning
Implement k-nearest neighbors, and perform non-parametric classification.
Clustering
Implement k-means clustering, and perform EM for Gaussian mixtures.
Support Vector Machines
Explain the advantages of Support Vector Machines and margin maximization.
Boosting
Explain boosting and decision tree models.
Neural Networks
Implement backpropagation for basic neural networks, and understand the concepts of deep neural networks.