andromeda0505

alphazero

A reimplementation of the Google AlphaZero algorithm.

Deep-Learning

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.

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.

Econometrics-methods

Econometrics and causal inference plays an important role in technology, particularly in areas such as machine learning and artificial intelligence. In these fields, the goal is often to understand the causal relationships between variables in order to make predictions or decisions.

interpretable-ml-book

Book about interpretable machine learning

Machine-Learning-Projects-

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Meta-heuristic-Optimization

Metaheuristic optimization deals with optimization problems using metaheuristic algorithms. Optimization is essentially everywhere, from engineering design to economics and from holiday planning to Internet routing. As money, resources and time are always limited, the optimal utility of these available resources is crucially important.

Modeling-an-inventory-routing-problem-for-perishable-products

A Python replication of an inventory routing problem article

nber_methods_lecture_slides

Slides from NBER Methods Lecture (extracted from the Wayback Machine)

Time-Series-Modeling-Techniques

Time series analysis is a statistical technique that is used to analyze and model time-based data. It involves identifying patterns, trends, and relationships in data that change over time, and using those patterns to make predictions about future observations.

andromeda0505/conformal-metalearners

[ NeurIPS 2023 ] Official Codebase for "Conformal Meta-learners for Predictive Inference of Individual Treatment Effects"

andromeda0505/conformal-predictions-from-scratch

Various Conformal Prediction methods implemented from scratch in pure NumPy for an educational purpose.

andromeda0505/Econometrics-methods

Econometrics and causal inference plays an important role in technology, particularly in areas such as machine learning and artificial intelligence. In these fields, the goal is often to understand the causal relationships between variables in order to make predictions or decisions.

andromeda0505/Machine-Learning-Projects-

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

andromeda0505/Meta-heuristic-Optimization

Metaheuristic optimization deals with optimization problems using metaheuristic algorithms. Optimization is essentially everywhere, from engineering design to economics and from holiday planning to Internet routing. As money, resources and time are always limited, the optimal utility of these available resources is crucially important.

andromeda0505/Modeling-an-inventory-routing-problem-for-perishable-products

A Python replication of an inventory routing problem article

andromeda0505/nber_methods_lecture_slides

Slides from NBER Methods Lecture (extracted from the Wayback Machine)

andromeda0505/Time-Series-Modeling-Techniques

Time series analysis is a statistical technique that is used to analyze and model time-based data. It involves identifying patterns, trends, and relationships in data that change over time, and using those patterns to make predictions about future observations.

andromeda0505/andromeda0505

andromeda0505/awesome-causal-inference

A curated list of causal inference libraries, resources, and applications.

andromeda0505/awesome-conformal-prediction

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

andromeda0505/Collusion-and-Algorithmic-Pricing

Research Proposal: The New Age of Collusion? An Empirical Study into Airbnb’s Pricing Dynamics and Market Behavior

andromeda0505/contextualbandits

Python implementations of contextual bandits algorithms

andromeda0505/ContinuousCausalCP

andromeda0505/ding_causalInference_python

python implementation of Peng Ding's "First Course in Causal Inference"

andromeda0505/doubleml-for-py

DoubleML - Double Machine Learning in Python

andromeda0505/grf

Generalized Random Forests

andromeda0505/Heckman-FA

Code repository of the paper "On Prediction Feature Assignment in the Heckman Selection Model" (to appear in IJCNN 2024)

andromeda0505/johansen

Python implementation of the Johansen test for cointegration

andromeda0505/machine-learning-articles

🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.

andromeda0505/Machine-Learning-with-Python

Python code for common Machine Learning Algorithms

andromeda0505/MetricsMLNotebooks

Notebooks for Applied Causal Inference Powered by ML and AI

andromeda0505/practical-bandits-tutorial

andromeda0505/Practical-Time-Series-In-Python

Practical guidance for time series analysis in Python

andromeda0505/probability

Probabilistic reasoning and statistical analysis in TensorFlow

andromeda0505/Python

All Algorithms implemented in Python

andromeda0505/Topics-In-Modern-Statistical-Learning

Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc

andromeda0505/UniversityOfCalgaryThesisTemplate

andromeda0505/UPfIE

Using Python for Introductory Econometrics

andromeda0505/web-advertising

Web Advertising BG - https://www.w3.org/community/web-adv/