SES #  TOPICS  KEY DATES 

1  Introduction  
2–5 
Nonnegative Matrix Factorization Discussion: When does wellposedness 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 averagecase assumptions? 
Final Project due on session 26 
Calendar
Instructor:  
Course Number: 

Departments:  
As Taught In:  Spring 2015 
Level: 
Graduate

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