Hill Climbing and Simulated Annealing AI Algorithms
What you'll learn
- Search algorihtms in Artificial Intelligence
- Hill Climbing algorithm
- Simulated Annealing algorithm
- Problem solving using search techniques
- Search and Optimization in AI
- Travelling Salesman Problem
- Test functions for benchmarking optimization algorithms
- Some programming backgroud will definatley help to underestand the coding videos
Search Algorithms and Optimization techniques are the engines of most Artificial Intelligence techniques and Data Science. There is no doubt that Hill Climbing and Simulated Annealing are the most well-regarded and widely used AI search techniques.
A lot of scientists and practitioners use search and optimization algorithms without understanding their internal structure. However, understanding the internal structure and mechanism of such AI problem-solving techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new algorithms for different projects.
This course is the easiest way to understand how Hill Climbing and Simulated Annealing work in detail. An in-depth understanding of these two algorithms and mastering them puts you ahead of a lot of data scientists. You will potentially have a higher chance of joining a small pool of well-paid AI experts.
Why learn optimization algorithms as a Data Scientist?
Optimization is getting popular in all industries every single month with the main purpose of improving revenue and decreasing costs. Optimization algorithms are extremely practical AI techniques in different projects. You can use them to automate and optimize the process of solving challenging tasks.
What does anyone need to learn about optimization?
The first thing you need to learn is the mathematical models behind them. You cannot believe how easy and intuitive the mathematical models and equations are. This course starts with intuitive examples to take you through the most fundamental mathematical models of all both Hill Climbing and Simulated Annealing. There is no equation in this course without an in-depth explanation and visual examples. If you hate math, then sit back, relax, and enjoy the videos to learn the math behind Neural Networks with minimum efforts.
It is also important to know what types of problems can be solved with AI optimization algorithms. This course shows different types of problems as well. There will be several examples to practice how to solve such problems as well.
What does this course cover?
As discussed above, this course starts straight up with an intuitive example to see what a Hill Climbing is as one of the most fundamental AI problem-solving approaches. After learning how easy and simple the inspiration and algorithms of Hill Climbing are, you will see how it performs in action live.
The second part of this course covers terminologies in the field of AI Optimization. In the third part, we will work with you on the process of designing Simulated Annealing using Hill Climbing. In the first three parts of this course, you master how the inspiration, theory, mathematical models, and algorithms of both Hill Climbing and Simulated Annealing algorithms.
In the last part of the course, we will implement both algorithms and apply them to some problems including a wide range of test functions and Travelling Salesman Problems.
By the end of this course, you will have a comprehensive understanding of Hill Climbing and Simulated Annealing and able to easily use them in your project. You can analyze, tune, and improve the performance of both techniques based on your project too.
Does this course suit you?
This course is an introduction to optimization and search in AI, so you need absolutely no prior knowledge in Artificial Intelligence, Machine Learning, or data science. However, you need to have a basic understanding of programming preferably in Matlab to easily follow the coding video. If you just want to learn the mathematical model and the problem-solving process using the two algorithms, you can then skip the coding videos.
Who is the instructor?
I am a leading researcher in the field of Optimization and AI. I have more than 150 publications including 100 journal articles, five books, and 20 conference papers. These publications have been cited over 15,000 times around the world. I was named as one of the most influential researchers in AI in 2019 by the Web of Science, the most well-regarded indexing organization in academia.
As a leading researcher in this field with over 15 years of experience, I have prepared this course to make everything easy for those interested in AI search and optimization. I have been counseling big companies like Facebook and Google in my career too. I am also a star-rising Udemy instructor with more than 5000 students and 1200 5-star reviews, I have designed and developed this course to facilitate the process of learning Hill Climbing and Simulated Annealing for those who are interested in this area. You will have my full support throughout your learning journey in this course.
There is no RISK!
I have some preview videos, so make sure to watch them see if this course is for you. This course comes with a full 30-day money-back guarantee, which means that if you are not happy after your purchase, you can get a 100% refund no question.
What are you waiting for?
Enroll now using the “Add to Cart” button on the right and get started today.
Who this course is for:
Professor Seyedali (Ali) Mirjalili is internationally recognized for his advances in Artificial Intelligence (AI) and optimization, including the first set of SI techniques from a synthetic intelligence standpoint - a radical departure from how natural systems are typically understood - and a systematic design framework to reliably benchmark, evaluate, and propose computationally cheap robust optimization algorithms. Prof. Mirjalili has published over 150 journal articles, many in high-impact journals, with one paper having over 4000 citations - the most cited paper in the Elsevier Advances in Engineering Software journal. In addition, he has more than five books, 30 book chapters, and 15 conference papers.
Prof. Mirjalili has over 25,000 citations in total with an H-index of 56. From Google Scholar metrics, he is globally one of the most-cited researchers in Artificial Intelligence. As the most cited researcher in Robust Optimization, he is in the list of 1% highly-cited researchers and named as one of the most influential researchers in AI by the world by Web of Science.
Ali is a senior member of IEEE and an associate editor of several journals including IEEE Access, Applied Soft Computing, Advances in Engineering Software, and Applied Intelligence. His research interests include Robust Optimization, Engineering Optimization, Multi-objective Optimization, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. He is working on the application of multi-objective and robust meta-heuristic optimization techniques as well.
In addition to his excellent research outputs, Prof. Ali has been a teacher for over 15 years and a Udemy instructor for more than three years. He has 10,000+ students, and the majority of his courses have been highly ranked by both Udemy and students. He is the only Udemy instructor in the list of top 1% highly-cited researchers.