anqizhang1/causalml-1

The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML

Causal Modeling in Machine Learning Workshop Repository

This is the workshop repository for the Causal Modeling in Machine Learning Workshop on Altdeep.ai.

Altdeep.ai workshop are taught via online instruction paired with one-on-one meetings with workshop instructors. Visit Altdeep.ai for info on the workshop content and fees. The material in this repo is open source and free.

Syllabi and Schedules

• NEU Syllabus

NEU Students: If you are thinking of signing up for the course and have questions, reach out to me at [email protected], rather than my NEU email.

Course Outcomes

Upon completing this course, you will be capable of building causal reasoning algorithms into decision-making systems in data science and machine learning teams within top-tier technology organizations. You will have implemented a portfolio project that demonstrates this competence. You will use the PyTorch-based probabilistic deep learning framework Pyro to implement the homework assignments and class project and will be an expert in using this framework by the end of the course.

Who is this course for?

You will gain the most from this course if you:

• You are familiar with random variables, joint probability distributions, conditional probabilities distributions, Baye's rule and basic ideas from Bayesian statistics, and expectation.
• You a good software engineer or aspire to be one.
• You work in or plan to work on a team running experiments in a top-tier tech company or a technically advanced retail company.
• You plan on working as an ML/AI research scientist and want to learn how to create agents that reason like humans.

Course Description

This course will cover the following:

1. Causality in the context of model-based machine learning, Bayesian modeling, and programmatic AI
2. Reasoning about probability distributions with directed acyclic graphs
3. Interventions and do-calculus, identification and estimation of causal effects, covariate adjustment, and other methods of causal inference
4. Counterfactual reasoning and algorithmic counterfactuals
5. Causal reasoning in the context of A/B tests, multi-armed bandits, sequential decision-making, and reinforcement learning
6. Deep causal latent variable models