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.

A curated list of awesome programming books (Algorithms and data structures, Artificial intelligence, Software Architecture, Humancomputer interaction, Operating Systems, Database Systems, IT Security, Concurrency, Interpreters and Compilers, High-Performance Computing, Distributed Systems, Game Development, Mathematical optimization)

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! )

VIP cheatsheets for Stanford’s CS 230 Deep Learning

Python implementation of algorithms from Russell And Norvig’s “Artificial Intelligence - A Modern Approach”

Lectures for INFO8006 - Introduction to Artificial Intelligence, ULige

VIP cheatsheets for Stanford’s CS 229 Machine Learning

Lisp code for the textbook “Paradigms of Artificial Intelligence Programming”