Dynamic Programming: Applications In Machine Learning and Genomics

عن

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.

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

Skills you learn

• Data Structures
• Dynamic Programming
• Algorithms
• Hidden Markov Model
• Genomics
• Machine Learning

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.

Auto Summary

Discover the fascinating intersection of dynamic programming and genomics with the "Dynamic Programming: Applications In Machine Learning and Genomics" course offered by edX. This engaging program delves into the use of dynamic programming techniques and Hidden Markov Models to compare genetic strings and explore evolutionary patterns. Designed for those with a budding interest in Data Science and AI, this course provides an awareness-level understanding, making it accessible to beginners. Under the expert guidance of experienced instructors, learners will navigate through the intricate applications of these computational methods within the realm of genomics. The course offers flexible subscription options, including Starter and Professional plans, allowing participants to choose the level of commitment that best suits their needs. With no specified duration, this course allows for self-paced learning, making it a convenient choice for busy individuals. Ideal for aspiring data scientists, bioinformaticians, and anyone interested in the cutting-edge applications of AI in biological research, this course promises to equip you with valuable insights and skills in dynamic programming and its impactful use in machine learning and genomics.