Lviv_Data_Science_School_2020_materials.md

Lviv Data Science Summer School 2020

Lviv Data Science Summer School 2020 Schedule

Bayesian Statistics

Bayesian modeling and related books:

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

• C.P. Robert: The Bayesian choice (advanced)

• Gelman, Carlin, Stern, Rubin: Bayesian data analysis (nice easy older book)

• Congdon: Applied Bayesian modelling; Bayesian statistical modelling (relatively nice books for references)

• Casella, Robert: Introducing Monte Carlo methods with R (nice book about MCMC)

• Robert, Casella: Monte Carlo Statistical Methods

• some parts of Bishop: Pattern recognition and machine learning (very nice book for engineers)

• Puppy book from Kruschke

• Mathematical Statistics

• Think Stats 2e

• Stratified sampling

Causal Inference

Correlation does not imply causation

More online lectures, courses, papers, books, etc. on Causality:

• Coursera:

  • A Crash Course in Causality: Inferring Causal Effects from Observational Data

• Powerful Concepts in Social Science playlists, Duke

• 4 lectures on causality by J.Peters (8 h), MIT Statistics and Data Science Center, 2017

• Causality tutorial by D.Janzing and S.Weichwald (4 h), Conference on Cognitive Computational Neuroscience 2019

• Course on causality by S.Bauer and B.Schölkopf (3 h), Machine Learning Summer School 2020

• Course on causality by D.Janzing and B.Schölkopf (3 h), Machine Learning Summer School 2013

• Causal Inference 3: Counterfactuals

• Causality for Machine Learning, Bernhard Schölkopf, 2019

• Elements of Causal Inference

• Causal Structure Learning,Christina Heinze-Deml, Marloes H. Maathuis, Nicolai Meinshausen, 2017

• Causal inference in statistics: An overview, 2009

• JUDEA PEARL, MADELYN GLYMOUR, NICHOLAS P. JEWELL CAUSAL INFERENCE IN STATISTICS: A PRIMER

• JUDEA PEARL - CAUSALITY, 2nd Edition, 2009

• Causation, Prediction, and Search, Second Edition

• Learning DAGs with Continuous Optimization

• Causality in cognitive neuroscience: concepts, challenges, and distributional robustness

• Active Invariant Causal Prediction: Experiment Selection through Stability, Juan L Gamella, Christina Heinze-Deml, 2020

• Investigating Causal Relations by Econometric Models and Cross-spectral Methods, 1969

• Fast Greedy Equivalence Search (FGES) Algorithm for Continuous Variables

• Greedy Fast Causal Inference (GFCI) Algorithm for Continuous Variables

• awesome-causality-algorithms

Casual Machine Learning (Papers):

• Causal Decision Trees, Jiuyong Li, Saisai Ma, Thuc Duy Le, Lin Liu and Jixue Liu, 2015
• Discovery of Causal Rules Using Partial Association, 2012
• Causal Inference in Data Science From Prediction to Causation, 2016

Experimental designs for casual learning:

• Matching
• Incident user design
• Active comparator
• Instrumental variables estimation
• Difference-in-differences
• Regression discontinuity design
• Modeling

Graph Neural Networks

Resources:

• DGL library
  • DGL notebook
  • DGL tutorial from TheWebConf-20 (WWW)
• Survey paper: D Bacciua et all (June 2020) A Gentle Introduction to Deep Learning for Graphs
• Survey paper: Z Wu et all (Dec 2019) A Comprehensive Survey on Graph Neural Networks
• [Series of posts](https://towardsdatascience.com/@michael.bronstein https://arxiv.org/abs/1901.00596)
• "Introduction to Graph Neural Networks" Zhiyuan Liu, Jie Zhou
• Lectures:
  • Follow related workshops, they release keynote videos, e.g.: https://www.aminer.cn/dl4g_www2020
  • Follow individual researchers, e.g. Jure Leskovec, Petar Veličković - a few lectures around "graph neural networks" online