CYB 3680 INTRODUCTION TO MACHINE LEARNING
The Introduction to Machine Learning course is designed for people who are new to the field of machine learning. It teaches the basics through easy-to-understand explanations and practical exercises. The course starts by explaining key concepts like supervised and unsupervised learning, classification, regression, and clustering. Students will also learn about different types of data and how to prepare it for machine learning. Later, they will explore various machine learning techniques like decision trees, support vector machines, k-nearest neighbors, neural networks, and understand how these techniques work and when to use them. The course teaches how to measure the performance of machine learning models, splitting data for training and testing, cross-validation, and how to understand metrics like accuracy, precision, recall, and F1 score. By the end of the course, students will have a solid foundation in machine learning, enabling them to understand and apply fundamental machine learning concepts, develop basic machine learning models, and critically evaluate and interpret machine learning results.
Cross Listed Courses
CIT 3680 /
CSC 3680
Prerequisite
A minimum grade of C in the following course:
CIT 2550.