Probabilistic Modeler in Three Months
Check out our recent webinar with Robert below
Time Left To Enroll:
What You'll Learn: Probabilistic Modeler in Three Months
The course sequence consists of five modules:
- Primer on Probabilistic Models and Bayesian Interference
- Probabilistic Programming Without Tears
- The Bayesian Model Building Workflow
- Models as Intellectual Property
- Posteriors as Intellectual Property
Who It's For
This course is designed to be practical and accessible for:
- Engineers and researchers who want to go beyond Bayes rule to Bayesian modeling workflow and philosophy
- Team leaders who want there team to uplevel in ways that go beyond importing new libraries
- Senior data scientists who want to connect modeling to business metrics and KPI
- Engineers who want models and inference algorithms as explicit code
- Executives who want to capture human domain knowledge as code
- AI practitioners who want an introduction to Bayesian cogsc
Enroll in Probabilistic Modeler in Three Months Today!
Don’t miss this opportunity to learn from one of the leading researchers and instructors in Probabilistic Modeling and Machine Learning. Secure your spot today as enrollment closes at 11:59pm PT on January 20th!
Click the button below to enroll via Robert’s page via altdeep.io. Use the discount code GOTWIML2021 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 workshops. Robert has put a ton of work into this sequence and will be providing TWIML learners with human support as they take the workshops. Rather than selling the modules individually, Robert offers enrollment in the full Probabilistic Modeling in Three Months for $1,199. This workshop sequence is designed to take you deeper into the practice of Bayesian Reasoning and Probabilistic ML.
The workshop will run from January 21st to April 22nd. 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 TWIML2021SPRING or TWIML2021SPRINGB (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 workshop, enrollment will be closed at 11:59pm PT on January 20th.
Robert’s weekly workshop 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.