Dynamic Programming: Applications In Machine Learning and Genomics

Overview

What you'll 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

Syllabus 8

1. Week 1: Pairwise Sequence Alignment
2. A review of dynamic programming, and applying it to basic string comparison algorithms.
3. Week 2: Advanced Sequence Alignment
4. Learn how to generalize your dynamic programming algorithm to handle a number of different cases, including the alignment of multiple strings.
5. Week 3: Introduction to Hidden Markov Models
6. 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.
7. Week 4: Machine Learning in Sequence Alignment
8. Formulate sequence alignment using a Hidden Markov model, and then generalize this model in order to obtain even more accurate alignments.

Skills you'll practise

Hidden Markov ModelData StructuresMachine LearningDynamic ProgrammingAlgorithmsGenomics

Advantages and disadvantages

Some points apply to every course of this kind; see how we rank.

Before you start

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

Taught by

• Pavel PevznerRonald R. Taylor Professor of Computer Science
• Phillip CompeauAssistant Teaching Professor