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This advanced course develops rigorous expertise in applying state-of-the-art artificial intelligence and machine learning methodologies to high-dimensional biological and biomedical data, with a translational emphasis on cancer detection (including liquid biopsy modalities) and therapeutic discovery. Students engage in advanced computational representations of biological sequences, multi-omics data, and molecular interaction networks; design and evaluate supervised, unsupervised, and self-supervised learning frameworks; and implement deep learning architectures—including convolutional neural networks and transformer-based models—for genomics, proteomics, and structure-function prediction tasks. The course further emphasizes end-to-end ML workflows for target identification, mechanism inference, and compound prioritization, with sustained attention to reproducibility, model interpretability, uncertainty quantification, and principled decision-making in biomedical research contexts.