Name: Moshtaba
Type: User
Company: University of Calgary
Bio: Statistics, Causal Machine Learning, Reinforcement Learning, Structural Estimation, Causal Inference & Optimization
Twitter: moshtabaes
Location: Homosapien, Earth, Milky Way Galaxy
Blog: https://twitter.com/moshtabaes
Moshtaba's Projects
Ray tutorials from Anyscale
A reimplementation of the Google AlphaZero algorithm.
Annealing is a mathematical and modeling method that is often used to help find a global optimization in a particular function or problem. Simulated annealing gets its name from the process of slowly cooling metal, applying this idea to the data domain. Simulated annealing is also known simply as simulated annealing.
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial Ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used.[2] Combinations of Artificial Ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing. The burgeoning activity in this field has led to conferences dedicated solely to Artificial Ants, and to numerous commercial applications by specialized companies such as AntOptima. --From Wikipedia
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
A curated list of awesome Matlab frameworks, libraries and software.
A curated list of awesome network analysis resources.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
The bacterial colony optimization algorithm is an optimization algorithm which is based on a lifecycle model that simulates some typical behaviors of E. coli bacteria during their whole life-cycle, including chemo-taxis, communication, elimination, reproduction, and migration. --From Wikipedia
:pencil: Markdown code for lots of small badges :ribbon: :pushpin: (shields.io, forthebadge.com etc) :sunglasses:. Contributions are welcome! Please add yours!
In Bee Colony Algorithm, a population based algorithm, the position of a food source represents a possible solution to the optimization problem and the nectar amount of a food source corresponds to the quality (fitness) of the associated solution. The number of the employed bees is equal to the number of solutions in the population. At the first step, a randomly distributed initial population (food source positions) is generated. After initialization, the population is subjected to repeat the cycles of the search processes of the employed, onlooker, and scout bees, respectively. An employed bee produces a modification on the source position in her memory and discovers a new food source position. Provided that the nectar amount of the new one is higher than that of the previous source, the bee memorizes the new source position and forgets the old one. Otherwise she keeps the position of the one in her memory. After all employed bees complete the search process, they share the position information of the sources with the onlookers on the dance area. Each onlooker evaluates the nectar information taken from all employed bees and then chooses a food source depending on the nectar amounts of sources. As in the case of the employed bee, she produces a modification on the source position in her memory and checks its nectar amount. Providing that its nectar is higher than that of the previous one, the bee memorizes the new position and forgets the old one. The sources abandoned are determined and new sources are randomly produced to be replaced with the abandoned ones by artificial scouts. --From Wikipedia
bikesharing with Tableau
Code and notebooks for my Medium blog posts
Python code for BLP (Berry, Levinsohn and Pakes) method of structural demand estimation using the random-coefficients logit model. Code for estimation of demand and supply-side moment jointly is also provided.
Authoring Books and Technical Documents with R Markdown
Replication code and downloadable example data sets for The Effect
A full example for causal inference on real-world retail data, for elasticity estimation
The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML
Uplift modeling and causal inference with machine learning algorithms
CBM Encoding
implement discrete choice model with Python from scratch
This repository hosts the code behind the online book, Coding for Economists.
Research Proposal: The New Age of Collusion? An Empirical Study into Airbnb’s Pricing Dynamics and Market Behavior
Conformalized Quantile Regression
Solutions to problems sent by dailycodingproblem.com
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent