Dynamic Programming: Applications In Machine Learning and Genomics

Обучение бесплатное
Сертификация платная
2 часа курса
О курсе

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.
Что Вы изучите?
  • 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
Pavel Pevzner
Pavel Pevzner
Ronald R. Taylor Professor of Computer Science The University of California, San Diego
Pavel Pevzner is Ronald R. Taylor Professor of Computer Science at the University of California, San Diego. He holds a Ph.D. from Moscow Institute of Physics and Technology, Russia. He is a Howard Hughes Medical Institute Professor (2006-present), an Association for Computing Machinery Fellow (2010), and an International Society for Computational Biology Fellow (2012). In addition to Bioinformatics Algorithms: An Active Learning Approach, he has authored the textbooks Computational Molecular Biology: An Algorithmic Approach (2000) and An Introduction to Bioinformatics Algorithms (2004) (jointly with Neil Jones).
Phillip Compeau
Phillip Compeau
Assistant Teaching Professor Carnegie Mellon University
Phillip Compeau is an Assistant Teaching Professor in the Carnegie Mellon University Computational Biology Department, where he serves as Assistant Director of the Master's in Computational Biology program. He holds a Ph.D. in mathematics from UC San Diego and completed his Master's degree at Cambridge University. Phillip co-founded Rosalind, an online platform for learning bioinformatics. A retired tennis player, he dreams of one day going pro in golf.
Эта платформа предоставляет все курсы бесплатно. Авторами выступают топовые университеты и корпорации, которые стараются удерживать стандарты качества. За несоблюдение дедлайнов, невыполнение домашнего задания студенты теряют баллы. Как и в других платформах, лекционные видео чередуются с практическими заданиями. Обучение проводится на английском, китайском, испанском, французском и хинди.
Dynamic Programming: Applications In Machine Learning and Genomics