Beijing Institute of Mathematical Sciences and Applications

This course offers a comprehensive introduction to statistical learning methods and their applications across diverse research domains, with particular emphasis on data-intensive problems in biological and biomedical sciences. It covers classical statistical learning methods, modern machine learning algorithms, and essential deep learning techniques, followed by real-world case studies from genomics, transcriptomics, phenomics, and network biology. Students will learn not only how to select, implement, and evaluate models, but also how to adapt them to high-dimensional, noisy, and heterogeneous biological datasets. The course includes literature review sessions focusing on landmark papers and state-of-the-art methods, enabling students to critically assess methodology and apply it to their own research.