Dynamic Programming: Applications In Machine Learning and Genomics
The University of California, San Diego
Edx
list 4 sequences
assignment Level : Intermediate
chat_bubble_outline Language : English
language Subtitles : English
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Key information

credit_card Free access
verified_user Fee-based Certificate
timer 32 hours in total

About the content

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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Prerequisite

Basic knowledge of:

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

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Syllabus

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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Intructors

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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The University of California, San Diego
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