Posted on 2020-02-12, by Germany2020.
h264, yuv420p, 1280x720 |ENGLISH, aac, 48000 Hz, 2 channels, s16 | 7h 05 mn | 7.03 GB
Instructor: Prof. Seyedali Mirjalili
Learn the main mechanisms of Genetic Algorithm as a heursitic Artificial Intalligence search or optimization in Matlab
What you'll learn
Use the Genetic Algorithm to solve optimization problems
Modify or improve the Genetic Algorithm
Analyze the performance of the Genetic Algorithm
Be familiar with the basics of programming
Be familiar with Matlab programming language
This is an introductory course to the Genetic Algorithms. We will cover the most fundamental concepts in the area of nature-inspired Artificial Intelligence techniques. Obviously, the main focus will be on the Genetic Algorithm as the most well-regarded optimization algorithm in history. The Genetic Algorithm is a search method that can be easily applied to different applications including Machine Learning, Data Science, Neural Networks, and Deep Learning.
With over 10 years of experience in this field, I have structured this course to take you from novice to expert in no time. Each section introduces one fundamental concept and takes you through the theory and implementation. The course is concluded by solving several case studies using the Genetic Algorithm.
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:
The inspiration of the Genetic Algorithm
Selection or survival of the fittest
Recombination or crossover
I am proud of 200+ 5-star reviews. Some of the reviews are as follows:
Femi said: "I really enjoyed the instructor style of explanation. He has a very solid understanding of the course and he made complex problems fun to solve."
Abhay said: "I tread cautiously when am taking MOOCs, as most of the courses offered fall short of what they claim they have to offer, eventually ending up quitting the course after making, say, 10% progress. But this course is different, after completing almost 80% - 90% of the course, I can say with confidence, that this is one of the most worthy courses I have undertaken on the topic of GA. The course is precise, relevant to the real-world problems and Ali is quite an engaging and prompt instructor. Hell, even the Possums in Australia would double that."
Ahmad said: "It was very nice experience. I think the course is designed very well for beginners like me because it starts with the basics. Then it gradually becomes more difficults. Overall it was a great course. Thanks Ali or Seyedali both of you :D :) if you know what I mean."
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:
This course is for everyone who wants to learn about optimization techniques
Both beginners and experts in Artificial Intelligence will benefit from this course since it covers both theory and application from basics to advanced topics
Those who wants to solve challenging optimization problems
Those curious about how the Genetic Algorithm solves optimization problems as one of the best and most well-regarded AI techniques in the world
- Ebooks list page : 42686
- 2020-04-14Introduction To Genetic Algorithms Theory And Applications
- 2020-03-30Introduction to Genetic Algorithms: Theory and Applications
- 2020-03-01Introduction to Genetic Algorithms: Theory and Applications
- 2020-02-11Introduction to Genetic Algorithms Theory and Applications
- 2011-08-09Functional integration theory and applications
- 2011-06-28Wireless Algorithms, Systems, and Applications: First International Conference, WASA 2006, Xi'an, China, August 15-17, 2006, Proceedings (Lecture Notes in Computer Science)
- 2011-11-04Parallel Genetic Algorithms: Theory and Real World Applications
- 2011-10-30Parallel Genetic Algorithms: Theory and Real World Applications (Studies in Computational Intelligence)
- 2011-10-05Parallel Genetic Algorithms: Theory and Real World Applications (Repost)
- 2011-09-02Introduction to Contextual Processing: Theory and Applications
- 2011-08-01Parallel Genetic Algorithms: Theory and Real World Applications (Studies in Computational Intelligence)
- 2011-07-26Parallel Genetic Algorithms: Theory and Real World Applications (Studies in Computational Intelligence)
- 2021-01-07Distributed Optimization, Game and Learning Algorithms: Theory and Applications in Smart Grid Systems
- 2019-12-30An Introduction to Soil Mechanics (Theory and Applications of Transport in Porous Media)
- 2019-07-15Harmony Search and Nature Inspired Optimization Algorithms Theory and Applications, ICHSA 2018
- 2019-01-11Constrained Clustering Advances in Algorithms, Theory, and Applications
- 2018-12-27Constrained Clustering Advances in Algorithms, Theory, and Applications
- 2018-06-02GIS Algorithms Theory and Applications for Geographic Information Science & Technology
- 2018-02-01[PDF] Bisociative Knowledge Discovery An Introduction to Concept, Algorithms, Tools, and Applications (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence) - Removed
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