This course offers an introduction to mathematical nonlinear optimization with applications in data science. The theoretical foundation and the fundamental algorithms for nonlinear optimization are ...

Some 350 researchers and students from more than 50 academic and industrial institutions discussed a range of questions about data science and machine learning at the first Princeton Day of ...

Learn how the Adagrad optimization algorithm works and see how to implement it step by step in pure Python — perfect for ...

Most optimization algorithms are based on quadratic approximations to nonlinear functions. You should try to avoid the use of funcions that cannot be properly approximated by quadratic functions. Try ...

Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, ...

Scientists found that ants and other natural systems use optimization algorithms similar to those used by engineered systems, including the Internet. These algorithms invest incrementally more ...

The massive datasets that power machine learning algorithms and systems are complex, noisy, and vulnerable to various kinds of errors, contamination, and adversarial corruptions. As data science and ...

Degree Requirements Students enrolled in the Professional MS in Computer Science (MSCPS) program with the Algorithms, Network and Optimization (ANO) subplan must complete 30 credit hours of graduate ...