MIT Deep Learning in Life Sciences Spring 2020

Overview

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.

Syllabus

MIT Deep Learning Genomics - Lecture 3 - Convolutional Neural Networks CNNs (Spring 2020).
MIT Deep Learning Genomics - Lecture 4 - Recurrent Neural Networks (Spring 2020).
MIT Deep Learning Genomics - Lecture 1 - Machine Learning Intro (Spring 2020).
MIT Deep Learning Genomics - Lecture 2 - Neural Networks and Gradient Descent (Spring 2020).
MIT Deep Learning Genomics - Lecture 5 - Model Interpretability (Spring 2020).
MIT Deep Learning Genomics - Lecture 6 - Regulatory Genomics (Spring 2020).
MIT Deep Learning Genomics - Lecture 7 - Regulatory Logic (Spring 2020).
MIT Deep Learning Genomics - Lecture 8 - Characterizing Uncertainty Expt Planning (S20).
MIT Deep Learning Genomics - Lecture 10 - Epigenomics 3Dgenome (Spring20).
MIT Deep Learning Genomics - Lecture 11 - RNA, PCA, t-SNE, Embeddings (Spring20).
MIT Deep Learning Genomics - Lecture 14 - Deep Learning for Gene Expression Analysis (Spring20).
MIT Deep Learning Genomics - Lecture 15 - Single-cell genomics (Spring 2020).
MIT Deep Learning in Genomics - Lecture 16 - Genetics 1: GWAS, Linkage, Fine-Mapping.
MIT Deep Learning Genomics - Lecture 17 - Genetics2: Systems Genetics.
How to present - Writing, Figures, Talks (MIT Deep Learning Genomics Lecture 22).