MIT Deep Learning in Life Sciences Spring 2020
Explore the intersection of deep learning and life sciences through this comprehensive MIT course. Delve into machine learning fundamentals, neural networks, and gradient descent before advancing to specialized topics like convolutional and recurrent neural networks. Gain insights into model interpretability, regulatory genomics, and regulatory logic. Investigate uncertainty characterization, experimental planning, epigenomics, and 3D genome analysis. Learn about RNA analysis, dimensionality reduction techniques, and embeddings. Discover applications in gene expression analysis and single-cell genomics. Study genetics, including GWAS, linkage, fine-mapping, and systems genetics. Conclude with valuable skills in scientific presentation, covering writing, figure creation, and effective talks.