Resolución de problemas por búsqueda

Universidad Nacional Autónoma de México via Coursera

Go to Course: https://www.coursera.org/learn/resolucion-busqueda

Introduction

**Course Review: "Resolución de problemas por búsqueda" on Coursera** If you're looking to deepen your understanding of problem-solving through search algorithms, the "Resolución de problemas por búsqueda" course available on Coursera is an opportunity you won't want to miss. This course offers a comprehensive overview of automated problem-solving techniques, guiding learners through the abstract representation of problems as state-action graphs and equipping them with the skills to select and adapt appropriate algorithms according to varying complexities. ### Course Overview "Resolución de problemas por búsqueda" focuses on utilizing search algorithms for automatic problem-solving. Throughout the course, students will learn how to represent problems as graphs, identify complexity parameters, and analyze computational resource consumption of various algorithms. A key emphasis is placed on applying these algorithms to concrete problems, providing a practical orientation to the theoretical concepts presented. ### Syllabus Breakdown The course syllabus is structured into key learning modules, each designed to build on the previous one: 1. **Algoritmos de Búsqueda ciega (Blind Search Algorithms)**: - In this section, participants will engage with uninformed search algorithms, considered foundational blocks for designing intelligent agents that tackle problems. Students will grasp how to abstract problems into state-action graphs and treat solutions as pathfinding within these graphs. Additionally, they will learn how to employ asymptotic analysis to evaluate algorithm performance, which is instrumental in understanding their efficiency. 2. **Algoritmos de búsqueda informada (Informed Search Algorithms)**: - This module introduces learners to informed search algorithms where domain knowledge is incorporated to guide the search process through heuristic functions. Understanding the impact of heuristic selection on the optimality of the paths found is critical in advancing one’s problem-solving strategy. 3. **Algoritmos de búsqueda metaheurísticos (Metaheuristic Search Algorithms)**: - As problem sizes increase in complexity, the effectiveness of the previously learned algorithms may diminish. This module focuses on metaheuristic algorithms, teaching students their importance in addressing large-scale problem complexities. You'll discover how these advanced strategies help navigate through vast solution spaces. ### Why You Should Take This Course 1. **Real-World Applications**: The course doesn't just dwell in theory; it emphasizes applying learned algorithms to real-world problems, making the content relevant and actionable. 2. **Expert Instruction**: The course draws on expert knowledge to guide you in implementing search algorithms effectively, ensuring a comprehensive educational experience. 3. **Enhanced Problem-Solving Skills**: With a hands-on approach to learning various algorithms, you'll be well-equipped to tackle a wide array of challenges, enhancing your analytical thinking and computational resource management abilities. 4. **Community Support**: By participating in the course, you join a community of learners and instructors who can provide support, share insights, and enhance your learning experience. 5. **Flexible Learning Environment**: Being an online course hosted on Coursera allows for self-paced learning, making it easy to fit your studies into your schedule. ### Conclusion In conclusion, "Resolución de problemas por búsqueda" is an indispensable course for anyone looking to master problem-solving through search algorithms. Whether you're a student aspiring to enter the field of artificial intelligence, a professional aiming to refine your algorithms and analytical skills, or simply someone with a passion for computational problem-solving, this course offers invaluable insights and practical skills. I highly recommend enrolling and embarking on a journey that could transform your approach to problem-solving in myriad contexts.

Syllabus

Algoritmos de Búsqueda ciega

Conocerás los algoritmos de búsqueda no informada. Estos algoritmos pueden verse como building blocks para diseñar agentes inteligentes que resuelven problemas. Aprenderás a abstraer los problemas como grafos de estados-acciones y podrás tratar la solución de problemas de manera equivalente a encontrar rutas dentro de este grafo. Te mostraremos cómo utilizar el análisis asintótico para evaluar el desempeño de los algoritmos.

Algoritmos de Búsqueda ciega (parte 2)

Conocerás los algoritmos de búsqueda no informada. Estos algoritmos pueden verse como building blocks para diseñar agentes inteligentes que resuelven problemas. Aprenderás a abstraer los problemas como grafos de estados-acciones y podrás tratar la solución de problemas de manera equivalente a encontrar rutas dentro de este grafo. Te mostraremos cómo utilizar el análisis asintótico para evaluar el desempeño de los algoritmos.

Algoritmos de búsqueda informada

Aprenderás a incorporar conocimiento del dominio del problema para guiar a los algoritmos en dirección a la solución por medio de funciones heurísticas. Entenderás el efecto que tiene la selección de la heurística en la optimalidad de las rutas encontradas.

Algoritmos de búsqueda informada (parte 2)

Aprenderás a incorporar conocimiento del dominio del problema para guiar a los algoritmos en dirección a la solución por medio de funciones heurísticas. Entenderás el efecto que tiene la selección de la heurística en la optimalidad de las rutas encontradas.

Algoritmos de búsqueda metaheurísticos

Comprenderás la importancia de los algoritmos meta-heurísticos para enfrentar la complejidad de problemas grandes donde los algoritmos vistos en los módulos anteriores dejan de ser efectivos.