Dynamic Programming: Applications In Machine Learning and Genomics

Dynamic Programming: Applications In Machine Learning and Genomics
2 hours course
Certification paid
Course by EdX
Dynamic Programming: Applications In Machine Learning and Genomics
What will you learn?
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
About the course

If you look at two genes that serve the same purpose in two different species, how can you rigorously compare these genes in order to see how they have evolved away from each other?

In the first part of the course, part of the Algorithms and Data Structures MicroMasters program, we will see how the dynamic programming paradigm can be used to solve a variety of different questions related to pairwise and multiple string comparison in order to discover evolutionary histories.

In the second part of the course, we will see how a powerful machine learning approach, using a Hidden Markov Model, can dig deeper and find relationships between less obviously related sequences, such as areas of the rapidly mutating HIV genome.

Dynamic Programming: Applications In Machine Learning and Genomics
Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.
Pavel Pevzner
Pavel Pevzner
Ronald R. Taylor Professor of Computer Science The University of California, San Diego
Phillip Compeau
Phillip Compeau
Assistant Teaching Professor Carnegie Mellon University
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