The application deadline for the course has now passed. We received a very promising number of submissions! Feel free to continue discussion in the comments below. Hey everyone! Gergely and Kōshin here from ALTER and Monastic Academy, respectively. We are excited to announce a 6-week, 4-hours-per-week mathematics course designed to introduce a few early topics in what we are calling mathematical AI alignment (roughly, classical learning theory up to embedded agency and infra-Bayesianism, though this course will cover just an initial portion of this) aimed at folks with the math proficiency level of a university math major (formal university degree not required). What this means is that if you have some mathematical dexterity, and wish that you could answer difficult questions on MDPs, POMDPs, learnability, sample complexity, bandits, VC dimension, and PAC learning, then this course may be for you. Lectures will be given over video call by Gergely, who has a math PhD from Stanford and has been working with Vanessa Kosoy over the past two years on the Learning Theoretic Agenda. Kōshin is helping with logistics. We are planning to have a one-hour video lecture each week, a one-hour discussion session each week, and a weekly problem set that should take around two hours to complete. The cost of the course is $200 per participant, which we’ll refund up to the full amount based on attendance. There is a question set for which we ask you to submit solutions in order to apply to join the course. This is to help us select a cohort with reasonably uniform mathematical proficiency, and to help us design material for that level of proficiency. We are looking for a cohort who are willing to make a strong commitment to joining all the lectures and discussion sessions, and solving all the question sets for the entire 6-week duration. You do not need to make that commitment in order to apply, but we’ll ask for it before the course begins. To get started right away, see the ent