18.409 | Spring 2015 | Graduate
Algorithmic Aspects of Machine Learning

Calendar

SES # TOPICS KEY DATES
1 Introduction  
2–5

Nonnegative Matrix Factorization

Discussion: When does well-posedness lead to better algorithms?

 
6–12

Tensor Decompositions

Discussion: When do algorithms rely (too much) on a distributional model?

Problem Set 1 due on session 9
13–17

Sparse Coding

Discussion: When does belief propagation (provably) work?

 
18–22

Learning Mixture Models

Discussion: Is nature an adversary? And if not, how can we model and exploit that?

Problem Set 2 due on session 20
23–26

Linear Inverse Problems

Discussion: Do we have enough average-case assumptions?

Final Project due on session 26
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Spring 2015
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