My Online Courses
Optimization Problems and Algorithms
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.
Combinatorial Optimization
Search methods and heuristics are of the most fundamental Artificial Intelligence techniques. One of the most well-regarded of them is Ant Colony Optimization that allows humans to solve some of the most challenging problems in history. This course takes you through the details of this algorithm.
Genetic Algorithms
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.
Artificial Neural Networks and Deep Learning
Machine learning is an extremely hot area in Artificial Intelligence and Data Science. There is no doubt that Neural Networks are the most well-regarded and widely used machine learning techniques. A lot of Data Scientists use Neural Networks without understanding their internal structure. However, understanding the internal structure and mechanism of such machine learning techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new Neural Networks for different projects. This course is the easiest way to understand how Neural Networks work in detail. It also puts you ahead of a lot of data scientists. You will potentially have a higher chance of joining a small pool of well-paid data scientists.
Multi-objective Optimization
This is an introductory course to multi-objective optimization using Artificial Intelligence search algorithms. We start with the details and mathematical models of problems with multiple objectives. Then, we focus on understanding the most fundamental concepts in the field of multi-objective optimization including but not limited to: search space, objective space, Pareto optimality, Pareto optimal solution set, Pareto optimal front, Pareto dominance, constraints, objective function, local fronts, local solutions, true Pareto optimal solutions, true Pareto optimal front, etc.
Hill Climbing and Simulated Annealing
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.
Grey Wolf Optimizer
This course is the easiest way to understand how one of the most popular AI optimization algorithms, the Grey Wolf Optimizer, works and solves optimization problems 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.
