UCSanDiegoX: Dynamic Programming: Applications In Machine Learning and Genomics
|
Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.
About this course
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.
At a Glance:
Institution: UCSanDiegoX
Subject: Computer Science
Level: Intermediate
Prerequisites:
Basic knowledge of:
at least one programming language: loops, arrays, stacks, recursion.
mathematics: proof by induction, proof by contradiction.
Language: English
Video Transcript: English
Associated programs:
MicroMasters® Program in Algorithms and Data Structures
Associated skills:Hidden Markov Model, Data Structures, Genomics, Machine Learning, Algorithms, Dynamic Programming
User Reviews
Be the first to review “UCSanDiegoX: Dynamic Programming: Applications In Machine Learning and Genomics”
Related Products
ETHx: Self-Driving Cars with Duckietown
Self-Driving Cars with Duckietown is the first robotics and AI MOOC with scale-model self-driving cars. Learn state-of-the-art autonomy hands-on: build your own real robot (Duckiebot) and get it to drive autonomously in your scaled city (Duckietown).
GTx: Data Structures & Algorithms IV: Pattern Matching, Dijkstra’s, MST, and Dynamic Programming Algorithms
Delve into Pattern Matching algorithms from KMP to Rabin-Karp. Tackle essential algorithms that traverse the graph data structure like Dijkstra’s Shortest Path. Study algorithms that construct a Minimum Spanning Tree (MST) from a graph. Explore Dynamic Programming algorithms. Use the course visualization tool to understand the algorithms and their performance.
IBM: Machine Learning with Python: A Practical Introduction
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
GalileoX: Java Programming Fundamentals
Your career as a program developer starts here. Gain entry-level programing skills in Java language, and prepare yourself to handle any task that is required in any programming job.
UCSanDiegoX: Data Structures Fundamentals
Learn about data structures that are used in computational thinking – both basic and advanced.
GTx: Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps
Become familiar with nonlinear and hierarchical data structures. Study various tree structures: Binary Trees, BSTs and Heaps. Understand tree operations and algorithms. Learn and implement HashMaps that utilize key-value pairs to store data. Explore probabilistic data structures like SkipLists. Course tools help visualize the structures and performance.
There are no reviews yet.