6.874/6.802/20.390/20.490/HST.506 Spring 2021 Prof. Manolis Kellis
Deep Learning in the Life Sciences / Computational Systems Biology
Playlist:    • MIT Deep Learning in Life Sciences - ...  
Latest slides and course today: http://compbio.mit.edu/6874
Spring 2021 slides and materials: http://mit6874.github.io/

0:00 Course intro, staff, meeting times, prereqs, grade breakdown, links
07:19 Why Deep Learning in the Life Sciences
23:49 Extracting signal from noise
25:24 Modules: ML, Regulation, Variation, Folding, Imaging, Frontiers
30:36 Lectures, Scribing, Quiz, Guest Lectures
34:10 Projects, mentoring, teams, milestones, papers, resources
51:28 First day survey, year, major, timezone, background, drivers, guidance
54:28 Intelligence, Classical AI, Artificial Intelligence, Machine Learning, Representations
1:00:30 Bayesian Inference, Observed vs. Hidden, Parameter Estimation
1:03:30 Bayes' Rule, Posterior, Likelihood, Prior, Marginal
1:05:40 Clustering, Classification, Feature Engineering, Feature Learning
1:08:27 Generative (model) vs. discriminative (separators) learning
1:11:09 Classification performance across range of thresholds
1:11:40 Network inference, linear algebra, dimensionality reduction, regularization
1:12:50 AI vs. Machine Learning vs. Representation Learning vs. Deep Learning
1:14:16 Deep representation learning through layers of abstraction
1:14:58 Human vision: layers, abstractions, representations, neuronal firing
1:16:20 Deep multi-layer architectures in mammalian, primate, and human brain
1:17:40 Neural network primitives, neurons, networks, non-linearities, gradient learning
1:18:23 Preview of backpropagation, overfitting, dropout, convolution, autoencoders
1:19:10 Conclusion, Questions, Compute Power of human, Interpretability, Goodbyes