I teach introductory data science, machine learning, and AI in Boston University’s Faculty of Computing & Data Sciences. The recent successes and pervasiveness of AI offer both challenges and opporunities in teaching these subjects.
An introduction to programming that also includes basic machine learning and complexity. Students learn workflows that combine human design with AI execution of the details, but they are required to be able to explain everything the AI did. Miniprojects throughout the semester allow student creativity while reinforcing concepts.
An introduction to classic ML algorithms such as decision forests and gradient boosting; neural networks, including PyTorch and transformer architectures; and AI methods, including agents and tool use. Students implement homework assignments that explore major AI/ML approaches, and also work on major projects for half the semester that involve exploring new techniques and student-chosen datasets.
These are the kinds of questions that currently animate my teaching and course design:
A sample DS110 lecture notebook, converted to PDF for browsing.
View sample lecture PDF →A sample DS110 homework notebook, converted to PDF for browsing.
View sample homework PDF →A sample DS340 lecture PDF on large language models.
View sample LLM lecture PDF →A sample DS340 LLM assignment notebook, converted to PDF for browsing.
View sample LLM assignment PDF →