Udemy - Optimization problems and algorithms [Course Drive]

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Udemy - Optimization problems and algorithms [Course Drive] (Size: 863.2 MB)
  1. Constrained optimization.mp4 29.8 MB
  1. Constrained optimization.srt 9.6 KB
  1. Discrete optimization.mp4 24 MB
  1. Discrete optimization.srt 7.1 KB
  1. Introduction.mp4 63.8 MB
  1. Introduction.srt 10.3 KB
  1. Optimization of problems with one objective.mp4 33.8 MB
  1. Optimization of problems with one objective.srt 15 KB
  1. Optimization problems.mp4 35.4 MB
  1. Optimization problems.srt 14.7 KB
  1. Theory of Particle Swarm Optimization.mp4 80.4 MB
  1. Theory of Particle Swarm Optimization.srt 22.9 KB
  1.1 ObjectiveFunction.m.m 409 B
  1.1 Part2-SOP.pdf.pdf 3.7 MB
  1.1 Part3-SOO.pdf.pdf 5.1 MB
  1.1 Part5_PSO.pdf.pdf 1.3 MB
  1.1 Part7_BinaryOptimization.pdf.pdf 576.5 KB
  1.2 DrawLandscape.m.m 409 B
  1.3 PSO.m.m 2.5 KB
  1.4 Part6_constraints.pdf.pdf 822.6 KB
  2. Coding a binary PSO.mp4.mtd 244.4 MB
  2. Coding a constrained objective function and how to solve it in Matlab.mp4 67.3 MB
  2. Coding a constrained objective function and how to solve it in Matlab.srt 20.8 KB
  2. History of optimization.mp4 23.6 MB
  2. History of optimization.srt 7.9 KB
  2. Implementing Particle Swarm Optimization in Matlab.mp4 131.8 MB
  2. Implementing Particle Swarm Optimization in Matlab.srt 39 KB
  2. Quiz 1.html 102 B
  2. Stochastic Optimization Algorithms.mp4 23.3 MB
  2. Stochastic Optimization Algorithms.srt 13.8 KB
  2.1 ObjectiveFunction.m.m 409 B
  2.1 PSO.m.m 1.9 KB
  2.1 Part1-History.pdf.pdf 858.5 KB
  2.1 Part4-Stochastic.pdf.pdf 2.4 MB
  2.2 DrawLandscape.m.m 409 B
  2.2 ObjectiveFunction.m.m 102 B
  2.3 PSO.m.m 2.5 KB
  3. PSO with bounding velocity.mp4 62.6 MB
  3. PSO with bounding velocity.srt 14.8 KB
  3. Quiz 2.html 102 B
  3. Quiz 4.html 102 B
  3.1 PSO.m.m 2.6 KB
  3.2 ObjectiveFunction.m.m 102 B
  4. Applying PSO to the table design problem.mp4 28.1 MB
  4. Applying PSO to the table design problem.srt 6.9 KB
  4.1 OurTableDesignProblem.m.m 102 B
  4.2 PSO.m.m 2.5 KB
  5. Quiz 3.html 102 B
  ReadMe.txt 204 B
  Visit Coursedrive.org.url 102 B
  ▲ 52 total files

Description


Optimization problems and algorithms

How to understand, formulate, and tackle the difficulties of optimization problems using heursitic algorithms in Matlab

What you'll learn

• Identify, understand, formulate, and solve optimization problems
• Understand the concepts of stochastic optimization algorithms
• Analyse and adapt modern optimization algorithms

Requirements

• You should have basic knowledge of programming
• You should be familiar with Matlab's built-in programming language
Description

This is an introductory course to the stochastic optimization problems and algorithms as the basics sub-fields in Artificial Intelligence. We will cover the most fundamental concepts in the field of optimization including metaheuristics and swarm intelligence. By the end of this course, you will be able to identify and implement the main components of an optimization problem. Optimization problems are different, yet there have mostly similar challenges and difficulties such as constraints, multiple objectives, discrete variables, and noises. This course will show you how to tackle each of these difficulties. Most of the lectures come with coding videos. In such videos, the step-by-step process of implementing the optimization algorithms or problems are presented. We have also a number of quizzes and exercises to practice the theoretical knowledge covered in the lectures.
Here is the list of topics covered:
• History of optimization
• Optimization problems
• Single-objective optimization algorithms
• Particle Swarm Optimization
• Optimization of problems with constraints
• Optimization of problems with binary and/or discrete variables
• Optimization of problems with multiple objectives
• Optimization of problems with uncertainties
Particle Swarm Optimization will be the main algorithm, which is a search method that can be easily applied to different applications including Machine Learning, Data Science, Neural Networks, and Deep Learning.
I am proud of 200+ 5-star reviews. Some of the reviews are as follows:
David said: "This course is one of the best online course I have ever taken. The instructor did an excellent job to very carefully prepare the contents, slides, videos, and explains the complicated code in a very careful way. Hope the instructor can develop much more courses to enrich the society. Thanks!"
Khaled said: "Dr. Seyedali is one of the greatest instructor that i had the privilege to take a course with. The course was direct to the point and the lessons are easy to understand and comprehensive. He is very helpful during and out of the course. i truly recommend this course to all who would like to learn optimization\PSO or those who would like to sharpen their understanding in optimization. best of luck to all and THANK YOU Dr. Seyedali."
Biswajit said: "This coursework has really been very helpful for me as I have to frequently deal with optimization. The most prominent feature of the course is the emphasis given on coding and visualization of results. Further, the support provided by Dr. Seyedali through personal interaction is top notch.

Boumaza said: "Good Course from Dr. Seyedali Mirjalili. It gives us clear picture of the algorithms used in optimization. It covers technical as well as practical aspects of optimization. Step by step and very practical approach to optimization through well though and properly explained topics, highly recommended course You really help me a lot. I hope, someday, I will be one of the players in this exciting field! Thanks to Dr. Seyedali Mirjalili."

Join 1000+ students and start your optimization journey with us. If you are in any way not satisfied, for any reason, you can get a full refund from Udemy within 30 days. No questions asked. But I am confident you won't need to. I stand behind this course 100% and am committed to help you along the way.

Who this course is for:

• Anyone who wants to learn optimization
• Anyone who wants to solve an optimization problem



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