What experience level is required for UCSanDiegoX: Dynamic Programming: Applications In Machine Learning and Genomics?

This course is aimed at the Intermediate level. Having some basic understanding of the topic will help you get the most out of the curriculum.

Is this course more theory-focused or project-focused?

This course provides a strong mix of conceptual theory and practical application to ensure a comprehensive understanding of the subject matter.

Do I get a certificate after completing UCSanDiegoX: Dynamic Programming: Applications In Machine Learning and Genomics?

Yes, a certificate of completion is included, which you can add to your professional profile or resume to showcase your newly acquired skills.

Biology & Life Sciences

UCSanDiegoX: Dynamic Programming: Applications In Machine Learning and Genomics

Name: UCSanDiegoX: Dynamic Programming: Applications In Machine Learning and Genomics
Price: 12450 INR
Availability: InStock

Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.

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Course Insight

Suitable for intermediate learners. Works well as a continuation after mastering Biology & Life Sciences fundamentals. It bridges the gap toward advanced, production-level engineering.

Intermediate FriendlyCertification IncludedSelf-Paced Learning

SKILLS TO
MASTER

Biology & Life Sciences Basics

Fundamental principles and concepts

Practical Application

Real-world project implementation

Best Practices

Industry standard workflows and guidelines

Problem Solving

Core Concepts

Implementation

Workflow Integration

Optimization

Careers:Relevant for professionals pursuing roles within Biology & Life Sciences.

Quick Facts

4 weeks

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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.

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Description

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.

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