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adaptive-filters

My collection of implementations of adaptive filters.

apmonitor-do

Dynamic Optimization

artificial-intelligence-projects

Collection of Artificial Intelligence projects.

awesome-causality-algorithms

An index of algorithms for learning causality with data

awesome-explainable-ai

A collection of research materials on explainable AI/ML

bayesian-online-changepoint-detection-for-multivariate-point-processes

Implementation of Log Gaussian Cox Process in Python for Changepoint Detection using GPFlow

causal-curve

A python package with tools to perform causal inference using observational data when the treatment of interest is continuous.

causaldiscoverytoolbox

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

causalinference.jl

Causal inference, graphical models and structure learning with the PC algorithm.

causality

chainer

A flexible framework of neural networks for deep learning

competition-baseline

数据科学竞赛各种baseline代码、思路分享

covid-19

cpde

Results of the research to reproduce

cpsc-540-project

curvelets

Curvelet-Transform based fibrillar collagen quantification (CurveAlign and CT-FIRE)

dais2

DAIS Website Revamp

data-competitions

Personal data competition experience and solutions

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

detectioncausalrelationshipstimeseries

Code for paper "A method for detecting causal relationships between industrial alarm variables using Transfer entropy and K2-Algorithm"

dfi-lwaer-soft-sensor

dicca_for_fault_diagnosis

dnn-cca

Deep neural network aided canonical correlation analysis (DNN-CCA) in Tensorflow and Keras

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.

ecg_pytorch

ecg classfication

econml

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

fault-diagnosis

Fault Diagnosis of Tennessee Eastman Chemical process using Neural Networks

github-covid-19

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

interpret

Fit interpretable models. Explain blackbox machine learning.