This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) ...

Understand the principles of efficient algorithms for dealing with large scale data sets and be able to select appropriate algorithms for specific problems. Understand and be able to apply the main ...

Dynamic programming (DP) algorithms have become indispensable in computational biology, addressing problems that range from sequence alignment and phylogenetic inference to RNA secondary structure ...

Banach spaces, as complete normed vector spaces, form a central framework in modern functional analysis. Their rich geometric structure underpins much of the theoretical development in approximation ...

This is an advanced undergraduate course on algorithms. This course examines such topics as greedy algorithms, dynamic programming, graph algorithms, string processing, and algorithms for ...

This course is available on the MPA in Data Science for Public Policy, MSc in Applicable Mathematics, MSc in Data Science, MSc in Operations Research & Analytics, MSc in Statistics, MSc in Statistics ...

Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...