Machine Learning Control: Genetic Algorithms
This lecture provides an overview of genetic algorithms, which can be used to tune the parameters of a control law. Machine Learning Control T. Duriez, S. L. Brunton, and B. R. Noack https://www.springer.com/us/book/9783319406237 Closed-Loop Turbulence Control: Progress and Challenges
From playlist Data-Driven Control with Machine Learning
Machine Learning Control: Genetic Programming
This lecture explores the use of genetic programming to simultaneously optimize the structure and parameters of an effective control law. Machine Learning Control T. Duriez, S. L. Brunton, and B. R. Noack https://www.springer.com/us/book/9783319406237 Closed-Loop Turbulence Control: Pr
From playlist Data-Driven Control with Machine Learning
9.10: Genetic Algorithm: Continuous Evolutionary System - The Nature of Code
In this video, I apply the Genetic Algorithm to an "Ecosystem Simulation", a system in which models biological life more closely, where elements live and die continuously evolving over time. 💻Code : https://github.com/CodingTrain/Rainbow-Code 🎥Previous video : https://youtu.be/Zy_obitkyO
From playlist Session 2 - Genetic Algorithms - Intelligence and Learning
9.2: Genetic Algorithm: How it works - The Nature of Code
In part 2 of this genetic algorithm series, I explain how the concepts behind Darwinian Natural Selection are applied to a computational evolutionary algorithm. 🎥 Previous video: https://youtu.be/9zfeTw-uFCw?list=RxTfc4JLYKs&list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV 🎥 Next video: https://yo
From playlist Session 2 - Genetic Algorithms - Intelligence and Learning
9.1: Genetic Algorithm: Introduction - The Nature of Code
Welcome to part 1 of a new series of videos focused on Evolutionary Computing, and more specifically, Genetic Algorithms. In this tutorial, I introduce the concept of a genetic algorithm, how it can be used to approach "search" problems and how it relates to brute force algorithms. 🎥 Next
From playlist Session 2 - Genetic Algorithms - Intelligence and Learning
9.6: Genetic Algorithm: Improved Fitness Function - The Nature of Code
In this video I look at strategies for improving the genetic algorithm's fitness function to improve efficiency and accuracy. https://thecodingtrain.com/more/archive/nature-of-code/9-genetic-algorithms/9.6-improved-fitness-function.html 🕹️ p5.js Web Editor Sketch: https://editor.p5js.org/
From playlist Session 2 - Genetic Algorithms - Intelligence and Learning
Continuous Genetic Algorithm - Part 1
This video is about Continuous Genetic Algorithm - Part 1
From playlist Optimization
Intro Into Multi Objective Optimization
Multi-objective optimization (also known as multi-objective programming, vector optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective func
From playlist Software Development
Transcription and Translation, excerpt 2 | MIT 7.01SC Fundamentals of Biology
Transcription and Translation, excerpt 2 Instructor: Eric Lander View the complete course: http://ocw.mit.edu/7-01SCF11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 7.01SC Fundamentals of Biology
John Novembre - Addressing challenges from next generation sequencing
PROGRAM: School and Discussion Meeting on Population Genetics and Evolution PROGRAM LINK: http://www.icts.res.in/program/PGE2014 DATES: Saturday 15 Feb, 2014 - Monday 24 Feb, 2014 VENUE: Physics Auditorium, IISc, Bangalore Just as evolution is central to our understanding of biology, p
From playlist School and Discussion Meeting on Population Genetics and Evolution
Binary Genetic Algorithm - Part 3: Decision Variables, Costs, Population, Natural Selection Process
This video is about Binary Genetic Algorithm - Part 3: Decision Variables, Cost Functions and Population and Natural Selection Process
From playlist Optimization
Ruby Midwest 2013 Computer, Program Theyself! by Zee Spencer
Robots. Unthinking. Unfeeling. Cold. Incapable of surprising us. Or are they? In this demonstration I'll be walking through how I use ruby to teach computers to surprise me with answers to problems I don't know how to solve. It draws heavily on testing to model behavior, stats, and rubies
From playlist Ruby Midwest 2013
Binary Genetic Algorithm - Part 2: Working Principle and Coding/Encoding Processes
This video is about Binary Genetic Algorithm - Part 2: Working Principle and Coding/Encoding Processes
From playlist Optimization
Machine Learning Control: Genetic Programming Control
This lecture discusses the use of genetic programming to manipulate turbulent fluid dynamics in experimental flow control. Machine Learning Control T. Duriez, S. L. Brunton, and B. R. Noack https://www.springer.com/us/book/9783319406237 Closed-Loop Turbulence Control: Progress and Cha
From playlist Data-Driven Control with Machine Learning
TUfast Eco Team's Longitudinal Cruise Controller
Maximize efficiency by using an algorithm-based driving strategy with a vehicle model and GPS track data. Maximilian Amm, Alexander Hammerl, and Maximilian Mühlbauer designed a velocity controller for their EducEco / Shell Eco Marathon vehicle. Hammerl and Mühlbauer join Christoph Hahn, of
From playlist MATLAB and Simulink Racing Lounge
9.9: Genetic Algorithm: Interactive Selection - The Nature of Code
In this genetic algorithms video, I discuss a technique known as "interactive selection" where the algorithm's fitness function is calculated based on user / viewer interaction. 💻Code : https://github.com/CodingTrain/Rainbow-Code 🎥Previous video : https://youtu.be/ETphJASzYes 🎥Next video
From playlist Session 2 - Genetic Algorithms - Intelligence and Learning
Big data, AI, the genome, and everything (sponsored by Microsoft) Vijay Narayanan (Microsoft)
The secret of life lies in our DNA. From Mendel to discovering the double helix structure of the DNA to decoding Chromosone 22 to the completion of the Human Genome Project, innovations in the field of molecular biology and genetics have empowered humanity with great powers to change and s
From playlist Strata + Hadoop World 2017 - San Jose, California
Sushmita Roy: "Regulatory network inference on developmental and evolutionary lineages"
Computational Genomics Winter Institute 2018 "Regulatory network inference on developmental and evolutionary lineages" Sushmita Roy, University of Wisconsin Madison Institute for Pure and Applied Mathematics, UCLA March 2, 2018 For more information: http://computationalgenomics.bioinfor
From playlist Computational Genomics Winter Institute 2018
SDS 607: Inferring Causality — with Jennifer Hill
#DataScience #CausalInference #BayesianStatistics We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science. This episode is brought to you by Pachyderm
From playlist Super Data Science Podcast
