Resolución de problemas por búsqueda
This course is part of Introducción a la inteligencia artificial.
This comprehensive course focuses on automated problem-solving through search algorithms, teaching students how to abstract problems as state-action graphs and analyze their complexity. You'll learn to evaluate computational resource consumption of different algorithms to select the most appropriate approach for specific problems. The curriculum covers both uninformed search methods like DFS and BFS, as well as informed approaches like A* and IDA*, and even introduces metaheuristic algorithms for complex problems. Through hands-on Python programming assignments, you'll implement these algorithms and apply them to concrete problems, culminating in solving the Rubik's Cube challenge. This practical approach ensures you gain both theoretical understanding and practical skills in algorithmic problem-solving.
4.6
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What you'll learn
Understand how to abstract problems as state-action graphs
Implement and analyze blind search algorithms like DFS and BFS
Master informed search techniques including A* algorithm
Design effective heuristic functions for specific problem domains
Apply iterative deepening strategies to optimize search performance
Implement metaheuristic algorithms for complex problem spaces
Skills you'll gain
This course includes:
2.2 Hours PreRecorded video
2 assignments
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There are 5 modules in this course
This course provides a comprehensive introduction to automated problem-solving using search algorithms. The curriculum is structured to build from fundamental concepts to advanced techniques. Students first learn to abstract problems as state-action graphs and understand algorithmic complexity analysis. The course then explores uninformed (blind) search algorithms including Depth-First Search (DFS), Breadth-First Search (BFS), and Uniform Cost Search (UCS), analyzing their strengths and limitations. Moving to informed search, students master the A* algorithm and learn to design effective heuristic functions. The final sections cover advanced techniques like Iterative Deepening A* (IDA*) and metaheuristic approaches such as Simulated Annealing and Genetic Algorithms, particularly useful for complex problems. Throughout the course, theoretical concepts are reinforced through Python implementations and practical applications, culminating in solving the Rubik's Cube challenge.
Algoritmos de Búsqueda ciega
Module 1 · 1 Hours to complete
Algoritmos de Búsqueda ciega (parte 2)
Module 2 · 4 Hours to complete
Algoritmos de búsqueda informada
Module 3 · 4 Hours to complete
Algoritmos de búsqueda informada (parte 2)
Module 4 · 4 Hours to complete
Algoritmos de búsqueda metaheurísticos
Module 5 · 3 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Introducción a la inteligencia artificial
Instructor
Maestro en Ciencias de la Complejidad
Stalin Muñoz Gutiérrez is a Master in Complexity Sciences from the Universidad Autónoma de la Ciudad de México and holds a degree in Computer Engineering from the Facultad de Ingeniería at the Universidad Nacional Autónoma de México (UNAM). His primary area of interest is Artificial Intelligence, where he has been involved in basic research projects since 1995 and teaches related subjects at UNAM's Faculty of Engineering. He is particularly interested in developing technologies for search and rescue tasks and has served as an academic advisor for robotics research projects at UNAM, participating in international competitions like RoboCup. Muñoz Gutiérrez is currently affiliated with the Centro de Ciencias de la Complejidad at UNAM. On Coursera, he teaches courses such as "Inteligencia artificial: proyecto final," "Razonamiento artificial," and "Resolución de problemas por búsqueda," focusing on AI and problem-solving techniques. Additionally, he leads a specialization in "Introducción a la Inteligencia Artificial," which covers various AI concepts and techniques, including machine learning and adaptive behavior.
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4.6 course rating
21 ratings
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