6.883 Advanced Machine Learning

SHARE:

Graduate
Units: 3-0-9
Prerequisites: 6.437, 6.438 or 6.867
Instructors:  Professor Stefanie Jegelka
Schedule:  MW11-12:30, room 36-144
 
Description
 
This subject qualifies as an Artificial Intelligence concentration subject.
 
Fundamental and research-level topics around machine learning problems mostly with discrete and combinatorial structures. Example problems, mathematical models and representations (structured prediction, estimation with sparsity structure, graphs, negative correlations and diversity, etc.) and suitable algorithmic techniques using smooth and non-smooth convex optimization, decomposition techniques, submodular and combinatorial optimization. The course will show connections between different techniques, and connections beyond machine learning.