Various forms of optimization play critical roles in machine 
learning methods. A majority of machine learning algorithms 
minimize empirical risk by solving a convex or non-convex 
optimization. Structured predictors solve combinatorial 
optimizations, and their learning algorithms solve hybrid 
optimizations. And new approaches for stochastic optimization 
have become integral in modern deep learning methodology. 

Students who take this course will study the latest knowledge 
and foundational concepts on optimization in machine learning, 
including theoretical analyses of optimization-based learning 
algorithms, theoretical bounds of discrete optimization for 
structured prediction, and recent discoveries about non-convex 
optimization methods. 

Students must have taken CS5824 Advanced Machine Learning or 
have an equivalent background.