Dynamic Programming: Applications In Machine Learning and Genomics
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assignment 等级:中级
chat_bubble_outline 语言 : 英语
language 字幕 : 英语
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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 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

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前提

Basic knowledge of:

  • at least one programming language: loops, arrays, stacks, recursion.
  • mathematics: proof by induction, proof by contradiction.

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课程大纲

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.

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教师

Pavel Pevzner
Ronald R. Taylor Professor of Computer Science
The University of California, San Diego

Phillip Compeau
Assistant Teaching Professor
Carnegie Mellon University

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内容设计师

The University of California, San Diego
The University of California, San Diego
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平台

Edx

EdX est une plateforme d'apprentissage en ligne (dite FLOT ou MOOC). Elle héberge et met gratuitement à disposition des cours en ligne de niveau universitaire à travers le monde entier. Elle mène également des recherches sur l'apprentissage en ligne et la façon dont les utilisateurs utilisent celle-ci. Elle est à but non lucratif et la plateforme utilise un logiciel open source.

EdX a été fondée par le Massachusetts Institute of Technology et par l'université Harvard en mai 2012. En 2014, environ 50 écoles, associations et organisations internationales offrent ou projettent d'offrir des cours sur EdX. En juillet 2014, elle avait plus de 2,5 millions d'utilisateurs suivant plus de 200 cours en ligne.

Les deux universités américaines qui financent la plateforme ont investi 60 millions USD dans son développement. La plateforme France Université Numérique utilise la technologie openedX, supportée par Google.

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