This course introduces the fundamental principles and practical applications of machine learning, a field that enables computers to learn patterns from data and make decisions or predictions. Students will explore key topics such as supervised and unsupervised learning, model evaluation, and advanced techniques like ensemble methods and neural networks. Practical assignments and projects will provide hands-on experience using machine learning libraries like Scikit-learn, TensorFlow, and PyTorch. By the end of the course, students will be equipped to build machine learning models, understand current research, and address real-world challenges across various domains.
P: COMP SCI 362 with at least a C grade
Spring.