school's contents

README.md

@@ -11,3 +11,54 @@ organized by [Daniel Acuna](https://acuna.io) and [Stephen David](https://hearin
 ## Structure
 
 ### Day 1: Introduction
+- Presentation: Opening and introduction to science of science
+- Teaching #1 Introduction to environment: Jupyterhub, Notebooks, Github repository
+  - Activity #1: Login into the system, run a notebook, save, and submit through nbgrader
+- Teaching #2: explore datasets (MAG sample), funding (Ying Ding), mentorship (Qing Ke), content (pubmed open access), images (pubmed open access)
+  - Activity #2: simple computation of citations, funding across years, mentorship, text, and images
+- Teaching #3: Introduction to Python: basic principles, loading libraries, debugging
+  - Activity #3: run simple program on Python, load data into Pandas, and run a simple regression
+
+### Day 2: Machine learning and artificial intelligence
+- Presentation: Overview of machine learning in science of science
+- Teaching #1: Probability, statistics, learning, errors, functions
+  - Activity #1: Different kinds of learning and functions
+- Teaching #2: Model complexity and interpretability
+  - Activity #2: show how to overfit, underfit, bias-variance tradeoff
+- Teaching #3: unsupervised learning, semi-supervised learning
+  - Activity #3: dimensionality reduction, NLP, reinforcement learning
+- Teaching #4: TBA
+  - Activity #4: TBA
+
+### Day 3: Network science
+- Presentation: Overview of Network Science
+- Teaching #1
+  - Activity #1
+- Teaching #2
+  - Activity #2
+- Teaching #3
+  - Activity #3
+- Teaching #4 
+  - Activity #4
+
+### Day 4: Deep learning
+- Presentation: Lucy Wang from Allen Institute of Artificial Intelligence (AI2)
+- Teaching #1: Neural networks (neurons and learning)
+  - Activity #1: Try neural network playground   
+- Teaching #2: Models for temporal data (BiLSTM, Transformers, etc)
+  - Activity #2: Citation worthiness prediction
+- Teaching #3: Models for image analysis (CNN, ResNet)
+  - Activity #3: image analysis, misleading graphs
+- Teaching #4: Bias in AI (Lizhen's presentation)
+  - Activity #4: Example of Bias in AI
+
+### Day 5: Causal inference
+- Presentation by Jianxuan Liu (Syracuse University)
+- Teaching #1: From correlation to causation - intuitive example
+  - Activity #1: Try discovering whether there is causality
+- Teaching #2: Methods for causal inference - theory of propensity score matching
+  - Activity #2: Simple example using PSM with logistic regression
+- Teaching #3: Difference in difference, regression discontinuity, matching
+  - Activity #3: Example from the literature (Aaron Clauset, Dashun's paper)
+- Teaching #4: Machine learning perspective (do-calculus), DAGs
+  - Activity #4: Backdoor, transportability, etc