CS 383/613 Machine Learning

This course covers the fundamentals of modern statistical machine learning. Lectures will cover fundamental aspects of machine learning, including dimensionality reduction, overfitting, ensemble learning, and evaluation techniques, as well as the theoretical foundation and algorithmic details of representative topics within clustering, regression, and classification (for example, K-Means clustering, Support Vector Machines, Decision Trees, Linear and Logistic Regression, Neural Networks, among others). Students will be expected to perform theoretical derivations and computations, and to be able to implement algorithms from scratch. Here is a short excerpt from the class describing what is machine learning…