• Dynamic programming and how it applies to basic string comparison algorithms
• Sequence alignment, including how to generalize dynamic programming algorithms to handle different cases
• Hidden markov models
• How to find the most likely sequence of events given a collection of outcomes and limited information
• Machine learning in sequence alignment

Week 1: Pairwise Sequence Alignment
A review of dynamic programming, and applying it to basic string comparison algorithms.

Week 2: Advanced Sequence Alignment
Learn how to generalize your dynamic programming algorithm to handle a number of different cases, including the alignment of multiple strings.

Week 3: Introduction to Hidden Markov Models
Learn what a Hidden Markov model is and how to find the most likely sequence of events given a collection of outcomes and limited information.

Week 4: Machine Learning in Sequence Alignment
Formulate sequence alignment using a Hidden Markov model, and then generalize this model in order to obtain even more accurate alignments.

“This is an extraordinary course. It requires commitment and a fair amount of time, but this is what implies the approach of guiding students step-by-step to implement themselves the algorithms. In my opinion, this is the best way to fully understand how algorithms work.”
-- Previous Student