STAT 05 — Statistics for AI, Machine Learning, and Data Science
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.
Knowledge of mathematics and statistics is recommended. Such foundational knowledge ensures students can grasp concepts and theories effectively. Enrolling in the course without prior education in these topics may lead to challenges in understanding and application of course content.