Dynamic Programming: Applications In Machine Learning and Genomics

Provided by The University of California, San Diego (UCSanDiegoX)
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Intermediate
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Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.

Course Format:Instructor-Led
Start Date:Mar 18, 2019

What you will 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

Overview

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.

Before you start

Basic knowledge of:

  • at least one programming language: loops, arrays, stacks, recursion.
  • mathematics: proof by induction, proof by contradiction.
  • Instructor-Led: course contains assignments and exams that have specific due dates, and you complete the course within a defined time period.
  • Course ends: Mar 18, 2019

Learner Testimonials

“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

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