Optimization problems and algorithms

Optimization problems and algorithms

BestSeller | h264, yuv420p, 1280x720 |ENGLISH, aac, 48000 Hz, 2 channels, s16 | 8h 03 mn | 863.26 MBInstructor: Prof. Seyedali MirjaliliHow to understand, formulate, and tackle the difficulties of optimization problems using heursitic algorithms in MatlabWhat you'll learnIdentify, understand, formulate, and solve optimization problemsUnderstand the concepts of stochastic optimization algorithmsAnalyse and adapt modern optimization algorithmsRequirementsYou should have basic knowledge of programmingYou should be familiar with Matlab's built-in programming languageDescriptionThis 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 algorithmsParticle Swarm Optimization Optimization of problems with constraints Optimization of problems with binary and/or discrete variables Optimization of problems with multiple objectivesOptimization 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 optimizationAnyone who wants to solve an optimization problem