Machine learning systems increasingly shape decisions in areas such as healthcare, education, finance, and public policy. For that reason, learning machine learning is not only about building accurate models, but also about understanding how those models are created, evaluated, and interpreted. This course places particular emphasis on reproducibility and ethical awareness as core components of undergraduate machine learning education. Reproducibility is introduced through hands-on practice. Students work in Jupyter notebooks, use open-source libraries, and document their workflows carefully so that results can be rerun and verified. Assignments and labs emphasize clear code structure, explanatory comments, and transparent evaluation metrics. By using open datasets and version-controlled repositories, students learn habits that…

Designing a Zero Textbook Machine Learning Course This course was designed as a Zero Textbook Course (ZTC) with the goal of making machine learning education more accessible, transparent, and equitable. Commercial textbooks in computer science—especially in rapidly evolving fields like machine learning—can be expensive, quickly outdated, and inaccessible to many students. By relying entirely on Open Educational Resources (OER), this course removes cost barriers while allowing students to engage with current, high-quality materials. Machine learning is particularly well-suited to open pedagogy. Many of the most influential textbooks, research papers, software libraries, and datasets in the field are already openly available. This course takes advantage of that ecosystem by using open…