STATISTICAL & MACHINE LEARNING.

Catalog description

This course offers an in-depth exploration of statistical and machine learning methods used in data analysis and predictive modeling. Students will learn about key algorithms and techniques, including linear and logistic regression, decision trees, support vector machines, ensemble methods, and neural networks. Emphasis is placed on both the theoretical understanding and practical implementation of these models, with a focus on selecting appropriate methods for different data types and problem contexts. Through hands-on projects and real-world datasets, students will develop the skills to build, evaluate, and interpret complex models, preparing them for advanced applications in data science and research.