About This Course

The course covers supervised and unsupervised learning, including regression, classification, and clustering, as well as an introduction to neural networks. Topics include data preprocessing, model evaluation, cross-validation, overfitting, and regularization. The research component addresses experimental design, hypothesis testing, literature review, research ethics, reproducibility, and the preparation of scientific reports and articles.

Upon completion of the course, students will be able to apply machine learning methods to data analysis tasks, design fair experiments, critically interpret findings, and communicate research results clearly. They will also be able to identify data leakage, assess model limitations, and support their conclusions with appropriate evidence.