Statistics for AI, Machine Learning, and Data Science

The machine learning that powers the models used in everything from autonomous vehicles to disease prediction hinges on understanding statistics and computer science. Those without at least a basic understanding of these fields can’t interpret a model’s output or make informed decisions about how to use it effectively. This course will give students a high-level understanding of some of the most common concepts in statistics that make AI and ML possible, including probability distributions, Bayes’s theorem, and entropy and information gain.Designed for those in technology or technology-adjacent roles, the course is split into two main sections. In the first section, students will explore foundational statistical concepts related to population and hypothesis testing, like A/B testing and p-value interpretation. The second section will cover topics ranging from linear regression to tree-based algorithms and cross-validation. These principles are explained using real-world examples from healthcare to marketing, ensuring contextual understanding. By the end of the course, students will have an understanding of standard statistical tools used in AI and ML algorithms and will be able to derive solid conclusions from ML models based on statistical studies.

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