6.890 Learning-Augmented Algorithms


Graduate Level
Units: 3-0-9
Prerequisites: 6.036 or equivalent, 6.046 or equivalent
Instructors:  Professors Costis Daskalakis and Piotr Indyk
Schedule: Lectures:  TR1-2:30, room 35-225
This subjects counts as a Theoretical Computer Science concentration subject.
The course focuses on recent developments in using machine learning to improve the performance of "classical" algorithms, by adapting their behavior to the properties of the input distribution. This reduces their running time or space use, increases the accuracy, etc. Specific topics include: learning-augmented data structures, streaming and sketching algorithms, on-line algorithms, compressive sensing and recovery, error-correcting codes, scheduling algorithms, satisfiability solvers and approximation algorithms.