Optimization is a crucial topic of Artificial Intelligence (AI). Getting an expected result using AI is a challenging task. However, getting an optimized result is more complicated. This course covers one of the commonly used optimization algorithms in AI – the Hill Climbing Algorithm. Optimization Using Artificial Intelligence: Hill Climbing Algorithm course will help you understand the problem space. Then convert it into a state-space landscape so that you can think mathematically model the problem space. Finally, it will guide you throughout the implementation process.
Prerequisite / Requirement:
This is an advanced-level course, and thus it requires the following prerequisites:
- Basic understanding of object-oriented programming language,
- Familiarity with Python programming language,
- Knowledge of AI Search algorithms,
- Knowledge of Linear Algebra.
After completing this course, you will have substantial knowledge of the working principle of the Hill-Climbing algorithm. You will also be able to apply it in optimizing real-world solutions. Apart from the theoretical concept, this lesson will help you build confidence to model real-world problems using object-oriented programming. Also, you will be able to apply the idea of AI in optimizing solutions. In a nutshell, after completing this lesson, you will have the following:
- Understanding the concept of the Hill-Climbing algorithm,
- Ability to convert a problem space into the state-space landscape,
- Understanding the domain of object and cost function,
- Specifying optimization goal based on the function nature,
- Finally, the ability to think in code and implement the concept using object-oriented programming.
- Lectures 19
- Quizzes 1
- Duration 1 hour
- Skill level Intermediate
- Language English
- Students 646
- Assessments Self