The Science of AI and ML

This course will teach you how to model data and use machine learning as a serious tool for analysis, discovery, and research. It is not simply about using an LLM, although modern AI tools can certainly be part of the workflow. The deeper goal is to understand the end-to-end nature of data science, machine learning, and artificial intelligence: how data is collected, cleaned, explored, transformed, modeled, tested, interpreted, and improved.

We will study both the practical side and the mathematical side. That means building models, evaluating performance, understanding errors, comparing methods, and learning why different algorithms behave the way they do. The course will cover core techniques in data science, supervised and unsupervised learning, model evaluation, feature engineering, statistics, artificial intelligence, and experimental thinking.

A major emphasis will be practice. You will work through assignments, exercises, applied projects, and research-style explorations designed to help you become comfortable using data to answer real questions. The course will also introduce newer and bolder techniques, including ways to test ideas, design experiments, and think critically about what a model is actually showing.

The purpose of this course is not just to teach software. It is to help you think like a data scientist: to explore the world carefully, ask better questions, build stronger models, and push toward deeper understanding.