Causal Modeling in Machine Learning
Check out our recent webinar with Robert below:
What You'll Learn: Causal Modeling in ML
The course sequence consists of six modules:
- Model-based Thinking in Machine Learning. Lay the foundation for causal models by deconstructing mental biases and acquiring new mental models for applied DS/ML.
- Do Causality like a Bayesian. Continue your “mental refactoring” by developing a Bayesian mental model for machine learning.
- How to Speak Graph; or DAG that’s a Nice Model! Become fluent in directed graphs and graph algorithms as a language of probability.
- The Tao of Do; Modeling and Simulating Causal Interventions. Learn to build your first causal generative machine learning model using a deep learning framework
- Applied Causal Inference; Identification and Estimation of Causal Effects from Data. Gain mastery of programmatic causal effect estimation.
- Counterfactual Machine Learning. Implement counterfactual reasoning algorithms in automated decision-making settings in industry.
Who It's For
One of the challenges facing those interested in learning about causality in ML is that most resources on the topic are geared towards the needs of statisticians or economists, versus those of data scientists and machine learning engineers. This course is designed to be practical and accessible for data scientists and engineers. It’s suitable for anyone looking to learn more about causal inference and apply it to real-world machine learning and data science challenges.
“I liked the course very much. Robert did a great job of reaching out to students to understand their background and interest in the course. It was great how he then continued to use what he learned about the students to make the course relevant and engaging to everyone enrolled. I also like how he made a connection to new paradigms. It was really nice to feel that the course is up to date. There are a lot of machine learning courses but this course was really special.”
— Bernhard – TWIML Study Group Participant
“I loved the course. I learned a ton and Robert was very available to students. When I think about how much I would have paid at my university for a similar course, TWIML is a great value.”
— Rose, TWIML Study Group Participant
“Robert is a great teacher. This is the first course I found that combines causal inference and machine learning. It does a great job of bridging the theoretical with what happens in the real world.”
— Shihgian, TWIML Study Group Participant
Enroll in ML Systems for Leaders & Innovators Today!
Don’t miss this opportunity to learn from one of the leading researchers and instructors in Causal Modeling and Machine Learning. Secure your spot today as enrollment closes at 11am ET on September 17th!
Click the button below to enroll via Robert’s page via altdeep.io. Use the discount codes TWIML2020FALL or TWIML2020FALLB (for the monthly payment plan) to take advantage of a 15% off discount for TWIML participants.
Note: This course is subject to a 30 day refund policy. We’re confident you’ll love it but if you’re unsatisfied for any reason we’ll issue a full refund within 30 days of purchase.
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 is currently a ML research scientist at Gamalon). 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.
These are paid courses. Robert has put a ton of work into this sequence and will be providing TWIML learners with human support as they take the courses. Rather than selling the modules individually, Robert offers enrollment in the full Causal Modeling in Machine Learning Track for $1,199. This course sequence is designed to take you deeper into the practice of causal ML.
The course will run from September 10th to December 10th. On time commitment, if you just want to go through lectures and videos, then the time commitment is akin to a deep read of one paper a week. If you wanted to work through code examples and assignments, then more. The course is designed to give you a level of depth that suits you.
Glad you asked. Yes, Robert has agreed to extend a 15% discount to TWIML community members who register using the links above. I suspect this is the lowest price these courses will ever be offered for. Please use the discount codes TWIML2020FALL or TWIML2020FALL (for the monthly payment plan) to get the TWIML participant discount.
Yes, we are an AltDeep / Teachable affiliate and get a commission as part of the partnership. Whatever we earn through this relationship will help support our broader community and educational efforts. That said, we would never recommend a course we didn’t think was a good use of your time and a good value.
After you enroll, you will have access to the materials indefinitely.
While the course itself is fundamentally designed for self-paced study, with Robert running a live weekly study group, enrollment will be closed on September 17th.
Robert’s weekly study group sessions are intended for enrollees and will assume that learners have at least gone over that week’s lectures at a high level.
Yes, the syllabus will roughly follow that of Robert’s Northeastern course, which you can find here.
The courses incorporate probabilistic programming concepts and use Pyro. From the Pyro web site:Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.
Please check our recent podcast, Causality 101, with Robert, here.