Machine Learning

Catalog description

Prerequisite: CMSC 310 AND MATH 216 Corequisite: none Description: Introduces fundamental concepts, algorithms, and practical applications of machine learning. Explores probabilistic modeling, underfitting, overfitting, regularization, and generalization, regression, classification tasks, and methods such as support vector machines, gradient descent, backpropagation, and neural networks, including a variety of neural network architectures. Covers recent advances in deep learning. Provides practical experience in implementing AI and ML algorithms using popular libraries.