In fall 2024, Advanced Topics in Machine Learning (ATML) will be taught by Amartya Sanyal, Amir Yehudayoff, and Rasmus Pagh. In the course we will go in depth into a number of selected topics in theoretical machine learning. In particular, the course will focus on a

• Advanced theoretical tools for design and analysis of machine learning algorithms. 

• Introduction of differential privacy 

• Differentially private machine learning algorithms. 

The course is relevant for computer science students as well as students from other studies with a good mathematical background, including Statistics, Actuarial Mathematics, Mathematics-Economics, Physics, etc. 

The course is theoretically oriented and assumes that you have a strong mathematical background. 

Important: We assume that the students have previously passed Machine Learning A+B courses offered by DIKU.

Recommended Academic Background: If you have a strong mathematical background and basic knowledge of machine learning it is possible to take ATML without taking ML-B. In this case, please, check taking ATML before/without ML and self-preparation instructions carefully. You should be able to solve the self-preparation assignment before joining the course, otherwise we strongly advise taking ML first. Be aware that our ML course is more theoretical than average machine learning courses and if you have taken a machine learning course elsewhere it does not necessarily prepare you for ATML. In particular, Introduction to Data Science (IDS) course given by DIKU is much lighter than ML and does not prepare you for ATML.

Practicalities

The course will be held in Block 1 Schedule C. Additional details regarding timing of lectures and tutorials are yet to be finalised.