Dynamic Programming: Applications In Machine Learning and Genomics

Dynamic Programming: Applications In Machine Learning and Genomics

Course
en
English
Subtitles available
32 h
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Source
  • From www.edx.org
Conditions
  • Self-paced
  • Free Access
  • Fee-based Certificate
More info
  • 4 Sequences
  • Intermediate Level
  • Subtitles in English

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Course details

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.

Prerequisite

Basic knowledge of:

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

Instructors

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

Phillip Compeau
Assistant Teaching Professor
Carnegie Mellon University

Editor

The University of California, San Diego

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