Machine Learning. (3-0) 3 Credit Hours

Catalog description

Prerequisite: EE 5153 ; or instructor’s approval. This course introduces the fundamental concepts of machine learning, including supervised, unsupervised, semi-supervised, and reinforcement learning paradigms. It presents both discriminative and generative learning, along with parametric and nonparametric models. The curriculum includes an examination of training, testing, and validation techniques such as cross-validation and statistical methods, including maximum-likelihood and maximum-a-posteriori-probability estimation. Students will have the opportunity to explore various regression models, including linear and non-linear approaches, regularization methods such as ridge and lasso, kernel techniques, logistic regression, classification algorithms, support vector machines, and the perceptron model. The course also focuses on unsupervised learning with a focus on dimensionality reduction, feature selection, and clustering. Potential advanced topics include multi-layer perceptrons, neural networks, stochastic parameter optimization, and backpropagation. The course also includes programming exercises and practical experimentation. This course has Differential Tuition.