Machine Learning.

Catalog description

In this course, students will gain a solid foundation in machine learning, exploring fundamental concepts such as supervised and unsupervised learning, model evaluation, and algorithm selection. The course covers key techniques including regression, classification, clustering, and dimensionality reduction, with an emphasis on both theoretical understanding and practical application. Students will learn to implement machine learning algorithms using programming languages like Python, applying popular libraries such as scikit-learn and TensorFlow to real-world datasets. Through hands-on projects, students will develop the skills to build, train, and optimize machine learning models. By the end of the course, students will be prepared to tackle machine learning challenges across diverse industries and pursue further study or careers in machine learning and AI. Prerequisite: EG1294 or EG1213.