Courses

There have been tremendous advancements in artificial intelligence (AI) and machine learning (ML) in recent years across a variety of fields ranging from autonomous driving to disease prediction and natural language processing. All of these advancements, however, are deeply rooted in the fields of statistics and computer science. This course will give students a high-level overview of some of the most common concepts in statistics that make AI and ML possible. Indeed, many of the newest algorithms, such as neural networks, random forests, and k-nearest neighbors, use statistics not only to build a model but also to evaluate its accuracy. The course will cover two broad areas of statistics: inference and prediction. The inference portion will introduce common statistical concepts that allow us to understand a population and test hypotheses (such as performing A/B tests and calculating and interpreting p-values). The prediction section will begin with the simplest of algorithms (linear regression) and gradually touch upon more advanced topics, such as random forests and cross-validation. Real-world examples from the fields of healthcare, genetics, marketing, and manufacturing will be used. By the end of the course, students will have a high-level understanding of common statistical tools used in AI and ML algorithms and will be able to derive their own conclusions from statistical studies.