Course Schedule#
Last updated May 31, 2023
Week 1 - 4/4, 4/6#
Lectures
Lecture 1: Logistics, Introduction to course, goals, biological data types, conceptual overview of deep learning and terminology (e.g. what are features, representations, tasks, etc.)
Lecture 2: Images as data, Sequences as data. Image augmentation with affine transformations. Representing sequences as one hot vectors
Recitations
Recitation 1: Cloud compute system intro. Introduction to assignment one.
Notebooks
Resources
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019). Deep learning for cellular image analysis. Nature methods, 1-14.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Eraslan, G., Avsec, Z., Gagneur, J., & Theis, F. J. (2019). Deep learning: new computational modelling techniques for genomics. Nature Reviews Genetics, 20(7), 389-403.
Week 2 - 4/11, 4/13#
Lectures
Lecture 3: Example Linear Classifier
Lecture 4: Conceptual overview of deep learning.
Recitations
Recitation 2: Intro to PyTorch. Basic linear MLP on MNIST.
Notebooks
Resources
Gamper, J., Koohbanani, N. A., Benet, K., Khuram, A., & Rajpoot, N. (2019). PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification. In European Congress on Digital Pathology (pp. 11-19). Springer, Cham.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
Week 3 - 4/18, 4/20#
Assignment
Lectures
Lecture 5: Linear classifier example: stochastic gradient descent, backpropagation, regularization, overfitting, underfitting, dataset splitting. Data annotation. Types of annotations for images - points, bounding boxes, dense pixel level labeling. Annotation for sequences.
Lecture 6: Problem framing. Real world tasks: Classification, segmentation, tracking, clustering. Model tasks: Classification, regression, vector embedding. How to frame real world tasks as model tasks
Recitations
Recitation 3: Intro to attention
Resources
Van Valen, D.A. et al. (2016). Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments. PLOS Computational Biology 12, e1005177.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
Week 4 - 4/25, 4/27#
Lectures
Lecture 7: Loss functions and optimizers. Classification - cross entropies, class imbalance, weighted cross entropy, focal loss. Regression - MSE. Optimizers - SGD, Momentum, RMSprop, Adam, Adam variants.
Lecture 8: Vision models. Model components - convolutions, pooling, activations, batch normalization. Initialization of weights. Components -> Layers. Model elements dealing with multiple spatial scales (U-Nets and FPNs). Themes in modern architectures - ResNets, DenseNets, Neural Architecture Search. Attention.
Recitations
Recitation 4: Project discussions
Notebooks
Resources
Simon Prince, Understanding Deep Learning (Chapter 5)
Jay Alammar, The Illustrated Transformer
Vaswani, Ashish, et al. (2017) Attention is all you need. In Advances in neural information processing systems 30.
Bai, M., & Urtasun, R. (2017). Deep watershed transform for instance segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5221-5229).
Week 5 - 5/2, 5/4#
Assignment
Lectures
Lecture 9: Guest Lecture by Kevin Yang (Microsoft Research)
Lecture 10: No lecture
Recitations
Recitation 5: Project discussions
Resources
Wu, K. E., Yang, K. K., Berg, R. V. D., Zou, J. Y., Lu, A. X., & Amini, A. P. (2022). Protein structure generation via folding diffusion. arXiv preprint arXiv:2209.15611.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).
Week 6 - 5/9, 5/11#
Assignment
assignments/assignment-4
Lectures
Lecture 11: Example: character level language modeling using MLP.
Lecture 12: Guest Lecture by Ross Barnowski
Recitations
Recitation 6: Project discussions
Resources
Ash, J.T., Darnell, G., Munro, D., and Engelhardt, B.E. (2021). Joint analysis of expression levels and histological images identifies genes associated with tissue morphology. Nature Communications 12, 1609.
Rives, A. et al. (2021). Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc Natl Acad Sci USA 118, e2016239118.
Week 7 - 5/16, 5/18#
Assignment
Lectures
Lecture 13: Tips and tricks I. Problem framing, data collection and curation, what architecture to choose, what loss function to choose, blending deep learning with existing methods.
Lecture 14: Tips and tricks II: How to debug when things go wrong. Follow the data schema for debugging deep learning workflows. Common errors and their fixes.
Recitations
Recitation 7: No recitation
Week 8 - 5/23, 5/25#
Lectures
Lecture 15: Guest lecture by Sam Cooper (Phenomic AI)
Lecture 16: Guest lecture by Gabriele Corso (MIT)
Recitations
Recitation 8: Intro to generative modeling: VAEs
Resources
Corso, G., Stärk, H., Jing, B., Barzilay, R., & Jaakkola, T. (2022). Diffdock: Diffusion steps, twists, and turns for molecular docking. arXiv preprint arXiv:2210.01776.
Karpathy, A. (2019). A Recipe for Training Neural Networks.
Week 9 - 5/30, 6/1#
Lectures
Lecture 17: Guest lecture by Brian Hie (Stanford/Meta)
Lecture 18: Project discussions
Recitations
Recitation 9: Basic VAE
Resources
Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., … & Rives, A. (2022, June). Learning inverse folding from millions of predicted structures. In International Conference on Machine Learning (pp. 8946-8970). PMLR.
Hie, B., Zhong, E. D., Berger, B., & Bryson, B. (2021). Learning the language of viral evolution and escape. Science, 371(6526), 284-288.
Week 10 - 6/6, 6/8#
Lectures
No lectures.