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Statistical Foundations for AI, Machine Learning, and Data Science

AI and machine learning rely on statistical principles essential for building, evaluating, and interpreting algorithms. This course provides a rigorous grounding in the methodologies underpinning contemporary data science. Students will explore prediction and classification models, progressing from linear regression and clustering to random forests, XGBoost, PCA, and transformer architectures. We will demystify complex systems, showing how advanced large language models (LLMs) share fundamental mathematical DNA with basic linear models. The focus is on statistical scaffolding and results interpretation, not a tutorial on training LLMs or building agents.This is a fast-paced, lecture-based course featuring live code demonstrations to show concepts in action. To maximize technical depth, there are no breakout rooms or in-class coding; datasets and notebooks are provided for independent homework. LILIYA LAVITAS Data Science Manager, Google Gemini