Causal Modeling in Machine Learning Workshop
The most efficient path to engineering causal reasoning in machine learning applications
A online workshop in causal modeling and causal inference in a machine learning context.
In contrast to a typical immersive in-person workshop, training, or boot camp, this course is designed for at-your-own-pace online learning, with short digestible course modules and lectures, but with enough depth to get full level mastery of the field.
- Continuous office hours via chat, instructor feedback on coding assignments
- Optional portfolio project in causal ML that you can showcase publicly. Get instructor guidance and iterative feedback on portfolio project.
- Gain ability to build causal reasoning algorithms into decision-making systems in data science and machine learning teams at top-tier technology organizations.
- Master modern programming libraries, including a deep learning framework, in the implementation of causal reasoning algorithms.
"I thoroughly enjoyed the course. I made friends along the way such as Jeremy, Harish and Nalin. Altdeep really shines on instructor office hours that allows students to ask questions. Students can DM the instructors directly. This is a differentiating factor for Altdeep. I interacted with other students and TAs on the forum a lot, which helped with learning. I also enjoyed the homework assignments and class project. Having a capstone project that student can put on their Github as portfolio is valuable. I have my name on Robert's Github for causal OOP, which is awesome! I also get to learn about other students’ projects. Double awesomeness!"
- Shih-Gian Lee, Senior Machine Learning Engineer
Frequently Asked Questions
StartProperties and Assumptions of Causal Modeling with DAGs
StartThe Causal Markov Property (3:12)
StartReichenbach’s Common Cause Principle
StartFaithfulness and Causal Minimality (1:06)
StartMarkov Equivalence (2:24)
StartPartially Directed Acyclic Graphs and Markov Equivalence (1:51)
StartCoding Equivalence and PDAGs (1:56)
StartCausal Sufficiency: How Big Should My Model Be? (2:01)
StartLatent Variables and Ancestral Graphs
Robert didn't start in machine learning. He started his career by becoming fluent in Mandarin Chinese and moving to Tibet to do developmental economics fieldwork. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies.
After switching to the tech industry, Robert's interests shifted to modeling data. He attained his Ph.D. in mathematical statistics from Purdue University, and then he worked as a research engineer in various AI startups. He has published in journals and venues across these spaces, including RECOMB and NeurIPS, on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems. In addition to startup work, he is a machine learning professor at Northeastern University.