This course offers students a practical introduction to using Machine Learning algorithms, tools, and techniques for solving problems that fall under the umbrella of Data Science. It is structured around learning concepts from the field of Machine Learning and applying them on data-intensive problems. While the course covers theories of Machine Learning and tools such as R, the focus is on using them for solving data-driven problems. Students will be introduced to several real-life problems that involve analyzing data for prediction, classification, organization, estimations, and pattern recognition.

Upon successful completion of this course, students will be able to:

• Exhibit familiarity with Machine Learning methods by learning and experiencing essential algorithms and approaches as they relate to data problems and information processing strategies;
• Use Machine Learning techniques to explore and analyze data, and derive decision-making insights;
• Identify data-driven analytics problems as they relate to organizational or individual information needs;
• Design innovative solutions and applications to solve problems using Machine Learning techniques and strategies.