MIT Deep Learning in Life Sciences Spring 2020

Overview

This course focuses on applying deep learning techniques in the field of life sciences, specifically genomics. By the end of the course, learners will be able to understand and implement Convolutional Neural Networks (CNNs), Recurrent Neural Networks, model interpretability, regulatory genomics, regulatory logic, epigenomics, RNA analysis, gene expression analysis, single-cell genomics, and genetics analysis. The course teaches skills such as machine learning, neural networks, gradient descent, characterizing uncertainty, and data visualization techniques. The teaching method includes lectures on various topics related to deep learning in genomics. This course is intended for individuals interested in the intersection of deep learning and life sciences, particularly genomics.

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).