MIT Deep Learning in Life Sciences Spring 2020
Massachusetts Institute of Technology via YouTube
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22
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Overview
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).
Taught by
Manolis Kellis
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Reviews
5.0 rating, based on 1 Class Central review
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Wonderful presentations with clear explanations. I loved watching the entire playlist. For my interest, I liked the lecture on linkage, but I would suggest revisiting the lectures even if you strongly think you know the basics.