Day 1 — Wednesday, October 16th

9:00 - 9:15		Registration/Breakfast
9:15 - 9:30		Introduction
9:30 - 10:00		Challenges in Single Cell Analysis
10:00 - 10:30		Thinking about High-Dimensional Data
		0.0. Plotting UCI Wine Data
10:30 - 11:00		Coffee Break
11:00 - 12:00		Introduction to Manifold Learning
		0.1. Learning Graphs from Data
12:00 - 1:00		Lunch
1:00 - 2:30		Preprocessing scRNAseq Data
		1.0. Preprocessing Embryoid Body Data
2:30 - 3:00		What is Visualization?
		2.0. Visualizing UCI Wine Data
3:00 - 3:30		Coffee Break
3:30 - 5:00		Creating better features with PCA
		2.1. PCA on Retinal Bipolar Data
		Nonlinear dimensionality Reduction
		2.2. Visualizing Retinal Bipolar Data
		2.3. Visualizing Embryoid Body Data
		2.4. Visualizing Simulated Data
5:00 - 6:00		Welcome & Networking

Day 2 — Thursday, October 17th

9:00 - 9:15		Introduction (breakfast provided)
9:05 - 9:15		Review of Manifold Learning
9:15 - 10:30		Clustering and Differential Expression
		3.0 Clustering Toy Data
10:30 - 11:00		Coffee Break
11:00 - 12:00		Reducing Noise in scRNAseq Measurements
		3.1 Clustering & Denoising Embryoid Body Data
12:00 - 1:00		Lunch
1:00 - 2:30		Identifying Developmental Trajectories
		4.0 Computing Diffusion Pseudotime
		4.1 Trajectory Inference in Fibroblast Data

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| 2:30 - 3:00 | | Information Theory for Gene-Gene Relationships | | 3:00 - 3:30 | | Coffee Break | | 3:30 - 5:00 | | 4.2 Identifying gene trends in Fibroblast Data | | | | 4.3 Trajectory Inference in EB Data | | | | 4.4 RNA Velocity | | | | 4.5 Gene regulatory inference during EMT |

Day 3 — Friday, October 18th

9:00 - 9:15		Introduction (breakfast provided)
9:15 - 10:30		Introduction to Neural Nets & Deep Learning
10:30 - 11:00		Coffee Break
11:00 - 12:00		Neural Network Classifiers & Autoencoders
		5.0 Classifying cell types with neural networks
		5.1 Exploratory data analysis with autoencoders
12:00 - 1:00		Lunch
1:00 - 2:30		Bring-your-own-data Workshop
4:30 - 5:00		Workshop Presentations
5:00		End of Class Celebration