MIT Compbio Lecture 12 - Deep Learning (Fall '19)

MIT Computational Biology: Genomes, Networks, Evolution, Health
http://compbio.mit.edu/6.047/
Prof. Manolis Kellis

Full playlist with all videos in order is here: https://www.youtube.com/playli....st?list=PLypiXJdtIca

All slides from Fall 2019 are here: https://stellar.mit.edu/S/cour....se/6/fa19/6.047/mate

Outline for this lecture:
1. Supervised Learning with Neural networks
- Perceptron, layers, activation units (sigmoid, softplus, ReLU)
- Learning: Gradient, Back-propagation, Rate, Dropout, Overfitting
2. Unsupervised learning with Deep belief networks & autoencoders
- Boltzmann machines, Restricted BMs (RBMs), Deep belief networks
- Learning: Energy, Gibbs Sampling, Simulated Annealing, Wake-sleep
3. Modern deep learning architectures
- Auto-encoders: Self-training, representation learning, RBM pre-training
- Convolutional neural networks: convolutional filters, pooling (sum/max)
- Recurrent neural networks: learning linear/temporal relationships
- Transfer learning, generative adversarial networks (GANs)
4. Deep learning in regulatory genomics
- Deciphering tissue-specific splicing code
- Deciphering regulatory grammars. DeepBind, DeepSea, Basset.
5. Three-dimensional structures: protein-DNA interactions, drug design
- Modern deep learning computing infrastructure
- Engines: TensorFlow, Theano, Torch, Caffe. Envs: Keras, Lasagene