CS 07556 - Machine Learning I

Credits: 3

This course introduces students to machine learning tasks at the graduate level including classification, regression, learning with unlabeled data), common machine learning approaches, and mathematics required to understand advanced topics in machine learning. Students will be exposed to topics such as data Issues in machine learning, Information-based learning (Decision Tree), Similarity-based learning (k-nearest neighbor), Probabilistic-based learning (naïve Bayes, Maximum A Posteriori, Bayesian Network), Linear Models (Perceptron, Linear Regression, Logistic Regression), Support Vector Machine, Neural Network, Performance measure and evaluation, Descriptive Statistics and Result Visualization, Learning with unlabeled data (clustering), Mathematics for Advanced Topics in Machine Learning (Topics in Probability, Linear Algebra, and Optimization).

Course Attributes: CAT, GCAT, GRAD