Recent advances in machine learning and artificial intelligence are affecting the work of data scientists and information systems professionals. Traditional artificial intelligence utilized rules-based, knowledge-based systems and logic programming. Today's artificial intelligence relies on machine learning methods and deep learning, in particular. Data science encompasses traditional statistics, operations research, and machine learning methods. Machine learning methods include naïve Bayes models, nearest neighbor models, classification and regression trees, random forests, support vector machines, and neural networks. Machine learning methods are data-adaptive—they learn from data. Advances in artificial intelligence rely on deep learning, which involves neural networks with many hidden layers learning from very large data sets. Artificial intelligence is a special area of study within data science and information systems. It has important applications in computer vision, natural language processing, and robotics.
Core Courses (4 units)
Specialization Courses (7 units)
About the Final Project
Students may pursue their capstone experience independently or as part of a team. As their final course, students take either the individual research project in an independent study format or the classroom final project class in which students integrate the knowledge they have gained in the core curriculum in a project presented by the instructor. In both cases, students are guided by faculty in exploring the body of knowledge on information systems while contributing research of practical value to the field. The capstone independent project and capstone class project count as one unit of credit.