This course covers the primary areas of machine learning and applies them to real world computation scenarios. The goal of this class is to build computer models that can produce useful information whether they are predictions, associations, or classifications. This course covers the theoretical and practical algorithms, basic concepts and paradigms, key techniques, challenges, and tricks of machine learning. It also explores examples of how machine learning is used/applied today in the real world and demonstrates the construction and use of machine learning algorithms. This course discusses recent applications of machine learning, such as to robotic control, speech recognition, face recognition, data mining, autonomous navigation, bioinformatics, and text and web data processing. It also fuses machine learning with other areas of artificial intelligence and robotics and demonstrates the use and viability of machine learning models and techniques in myriad areas of science, technology, engineering, and management.