Project report and presentation: see project website.This is to encourage you to go through the solutions carefully while self-grading. The discrepancy between your own and our score for the selected problem will enter the final homework grade. rest is self-graded with mandatory hand-in.This will be primarily learned via the final presentation and report as well as during peer-grading of peer talks. This will be practiced in lectures / exercise and homeworks and potentially in the final project.Įffectively communicate and present the problem motivation, new insights and results to a technical audience. This will be an integral part of the project work and involves experimental as well as theoretical questionsįind and outline an approach (some subproblem) to prove a conjectured theorem.

For this purpose, students will learn common mathematical techniques from statistics and optimization in the first part of the course and apply this knowledge in the project workĬritically examine recently published work in terms of relevance and determine impactful (novel) research problems.

find high-impact questions and theorems to prove and work on that you are highly passionate aboutĪcquire enough mathematical background to understand a good fraction of theory papers published in the typical ML venues.

both easily read and write theorems that provide generalization guarantees for machine learning algorithms.Content wise, the technical part will focus on generalization bounds using uniform convergence, and non-parametric regression. This course is designed to prepare Master students for successful research in ML, and prepare PhD students to find new research ideas related to ML theory. Please sign up so that you can start working on a joint sheet right away. hackmd We will use this for some collaborative work.gradescope enroll with entry code 5VRB46 (homeworks).It will also be used for all announcements, discussion of homeworks and lectures, and parts of the assignments. Please ask questions in campuswire, eternal gratitude from your peers is ensured ).E-mails will most likely not be responded to if it’s a logistical inquiry - please post privately on campuswire.Office hours: online over zoom upon request via email.Konstantin Donhauser (konstantin.donhauser at ), Parnian Kassraie (parnian.kassraie at ).Lectures will also be recorded and posted online.Location of interactive sessions: online over gather.town (only works with a computer, not with the phone) see schedule.Location of lectures: for now, online over zoom (passcode given via email).

Homeworks qs 14.5