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…