Frequently Asked Questions
When does the course start and finish?
Cohorts are loosely aligned with American university semester schedules (Spring starting January, Summer starting in May/June, Fall starting in September). You can schedule appointments with instructors during you enrollment period. However, the course material never ends! It is a completely self-paced online course - you decide when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any devices you own.
Will you refund me if I'm unhappy?
Yes! We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days, and we will give you a full refund.
What do you mean by "online workshop"? What kind of access will I have to course instructors?
Altdeep runs online workshops. A workshop has an enrollment period. During the enrollment period you get access to instructors through the course forum. This includes group chats and one-on-ones (by appointment). Topics can range from coding assignment help to general questions and advice. You also get homework and project work checked by humans. You get guidance and frequent feedback on portfolio projects. You receive guidance and frequent feedback on portfolio projects. Finally, you have the ability to engage with a community of other students.
Where can I check out the course materials?
The course materials are available in the course repository at https://www.github.com/robertness/causalML.
What programming languages and libraries do we use in the workshop?
In this workshop, we rely primarily on a Pytorch-based probabilistic programming language called Pyro. We use some graphical modeling software in Python (pgmpy) and R (bnlearn).
How does the class project work?
If you like, instructors will work with you on a capstone project. You can see examples of previous projects by previous students here: https://bit.ly/causal_projects
What would you say would be a reasonable estimate for the (weekly) amount of time required to get a good grasp of the materials?
We designed this workshop so that you may remain at a high-level view if you prefer, and you may go deeper and get a deep grasp of the material if you prefer. For the high-level approach, you just watch videos, and read lecture notes, read through the forum, and ask questions in calls with the instructors. That requires about a half-hour a day for the four month enrollment period.
A deeper study is where you do the homework and the capstone project. That level of commitment is the same as a graduate school level course, which is 6-10 hours a week.
A deeper study is where you do the homework and the capstone project. That level of commitment is the same as graduate school level course, which is 6-10 hours a week.
What is the basic mathematical background I need to succeed?
You nee a basic understanding of random variables, joint probability, conditional probability, and expected value. You also want an understanding of Baye's rule and intuition about Bayesian reasoning. Finally, you should have an understanding of probability mass functions (plug in an outcome, get the probability of that outcome).
Will the lectures change?
Yes. We update the lectures with new videos and content based on developments in the field and feedback from students.
I do not have software engineering-level Python skills. Is this workshop right for me?
Yes. You just need to be comfortable with a language like Python, R, or Julia to do a data science-style analysis. The biggest coding challenge is usually debugging code when working on homework. For that, you can get help from instructors.
I am familiar with/interested in a particular causal inference method. I am interested in how it fits into the big picture. Will you cover this method?
Yes! Your interest is a signal that we should cover it, so we will if you ask.
How do I interact with the instructors and other students
Upon enrollment you will be able to access a course forum. The forum is a great place to discuss workshop materials with other students, instructors, as well as participate in a community of like-minded individuals.
How does this course compare with X course?
We strive to connect probabilistic modeling with causal modeling. This focus, and the focus on machine learning and AI, are the main differentiators in terms of content from other courses.
Importantly, this workshop features weekly retrospective calls, grading of assignments by humans, direct access to instructors, and access to an active community forum.
I don't want to pay the fee. How do I learn causal modeling for free?
Checkout the workshop repository at www.github.com/robertness/causalml for free tutorials. If you are looking for good textbooks, we recommend Elements of Causal Inference: Foundations and Learning Algorithms by Peters, Janzing, and Scholkopf.