Wei-Chen.Chen's repositories
pycounts
To learn how to develop my own python package systematically. It is a practice.
HopCPT
Conformal Prediction for Time Series with Modern Hopfield Networks
machine_learning_refined
Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
causality
Notes, exercises and other materials related to causal inference, causal discovery and causal ML.
Causal-Inference-and-Discovery-in-Python
Causal Inference and Discovery in Python by Packt Publishing
NumericsOfML
Notes for the Numerics of Machine Learning Lecture Course at the University of TĂĽbingen
ConformalPrediction.jl
Uncertainty quantification through conformal prediction for machine learning models trained in MLJ.
Econometrics.jl
Econometrics in Julia
ProgrammingCourse-with-Julia-SimulationAnalysisAndLearningSystems
Programming of Simulation, Analysis, and Learning Systems Course Materials
reinforcement-learning
A Gentle Principled Introduction to Deep Reinforcement Learning
cornell-cs5785-2020-applied-ml
Teaching materials for the applied machine learning course at Cornell Tech (online edition)
Grad-IO
Graduate Empirical Industrial Organization
JuliaHLRS22
Julia for High Performance Computing Course @ HLRS
graph_cp
Conformal prediction for node classification
conformal-prediction
Lightweight, useful implementation of conformal prediction on real data.
Python-Socket-Practice
Learning how to build a connection between Server-side and Client-side.
conformal_training
This repository contains a Jax implementation of conformal training corresponding to the ICLR'22 paper "learning optimal conformal classifiers".
AI-For-Beginners
12 Weeks, 24 Lessons, AI for All!
SciMLBook
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
RiskMeasures.jl
Julia code for computing monetary measures of risk, coherent or not
Causal-Inference-1
Causal Inference 1 Mixtape Session
Shift-Share
Shift-Share Instrument Mixtape Track
Instrumental-Variables
Instrumental Variables Mixtape Track
ml-pen-and-paper-exercises
Pen and paper exercises in machine learning
bayes
Neat Bayesian machine learning examples
fall-in-love-with-julia
An introductory 101 series to get to know the power of Julialang
dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.