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aif360

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

awesome-fairness-in-ai

A curated list of awesome Fairness in AI resources

covid19-simulations

Some network simulations for the Coronavirus

deep-sad-pytorch

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

douzero

[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | 斗地主AI

fairautoml

A Fairness-aware AutoML System

hw-acceptance-unit-test-cycle

hw-bdd-cucumber

hw-rails-intro

hw-ruby-intro

Ruby Introduction Assignment for Agile Development using Ruby on Rails

machine_learning_examples

A collection of machine learning examples and tutorials.

medicalpersistancesystem2020

CSCE 606 Project for Medical Persistance System

meta-aad

[ICDM 2020] Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning

mia1996.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

multi-agent

Project 2: Multi-Agent Pacman - now with ghosts, heuristics including minimax, expectimax, & evaluation.

mynewproject

This is my new project

network-growth-models

Some simulations of network models, with focus on preferential attachment

python-random-quote

A file-based quote bot written in Python

r-rlcard_test

rapid

[ICLR 2021] Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments.

relation-classification-using-bidirectional-lstm-tree

TensorFlow Implementation of the paper "End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures" and "Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths" for classifying relations

rlcard

Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.

rlcard-tutoirial

rottenpotatoes-rails-intro

RottenPotatoes app skeleton for saasbook/hw-rails-intro

statsmodels

Statsmodels: statistical modeling and econometrics in Python

text-scraping-document-clustering-topic-modeling

The objective of this project is to scrape a corpus of news articles from a set of web pages, pre-process the corpus, and then to apply unsupervised clustering algorithms to explore and summarise the contents of the corpus. Part 1. Text Data Scraping This part of the project should be implemented as a Python script 1. Identify the URLs for all news articles listed on the website: http://mlg.ucd.ie/modules/COMP41680/news/index.html 2. Retrieve all web pages corresponding to these article URLs. 3. From the web pages, extract the main body text containing the content of each news article. Save the body of each article as plain text. Part 2. Corpus Exploration Tasks to be completed in your IPython notebook: 1. Load the text corpus generated in Part 1. Apply any appropriate pre-processing steps and construct a document-term matrix representation of the corpus. 2. Summarise the overall corpus by identifying the most characteristic terms and phrases in the corpus. 3. Apply two alternative clustering algorithms of your choice to the document-term matrix to produce clusters of related documents. This might require applying each algorithm several times with different parameter values. 4. For each clustering generated in Step 3, summarise the contents of the clusters. Based on your summary, suggest a topic/theme for each cluster.

tods

TODS: An Automated Time-series Outlier Detection System

user-machine-learning-tutorial

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html

visualization_for_wuhan-coronavirus_2019-ncov_cases_hk_networkx

Relationship visualization for Wuhan Coronavirus (2019-nCov) cases in Hong Kong by NetworkX