Razonamiento artificial
Master formal reasoning methods in AI through logic and probability theory, covering practical applications with Python implementation.
Master formal reasoning methods in AI through logic and probability theory, covering practical applications with Python implementation.
This comprehensive course explores formal reasoning in artificial intelligence through two main approaches: logic (deductive reasoning) and probability theory (for handling uncertainty). Students will master three logical systems and three probabilistic graphical models, gaining a solid foundation in both theoretical concepts and practical applications. The curriculum includes propositional logic, temporal logic, predicate logic, Bayesian networks, Markov chains, and Markov decision processes. Through hands-on programming assignments in Python, learners will implement key algorithms like DPLL and create probabilistic models, developing the essential reasoning skills required for advanced AI applications. This intermediate-level course balances theoretical knowledge with practical implementation, making it ideal for students with some prior experience in computer science and basic programming skills.
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What you'll learn
Understand and apply propositional logic concepts including syntax, semantics and inference
Implement the DPLL algorithm for logical reasoning
Master temporal logic for model verification in AI systems
Understand predicate logic as a foundation for various AI techniques
Apply Bayesian networks for probabilistic reasoning under uncertainty
Implement and utilize Markov chains for sequential data modeling
Skills you'll gain
This course includes:
1.9 Hours PreRecorded video
7 assignments
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There are 6 modules in this course
The Razonamiento artificial (Artificial Reasoning) course provides a comprehensive exploration of formal reasoning methods essential to artificial intelligence. The curriculum is divided into two main components: logical reasoning and probabilistic reasoning. In the logical reasoning section, students study propositional logic, temporal logic, and predicate logic, learning formal syntax, semantics, and inference techniques including the DPLL algorithm. The probabilistic reasoning portion covers Bayesian networks, Markov chains, and Markov decision processes, providing a foundation in uncertainty handling and decision-making under uncertainty. The course includes both theoretical lectures and practical programming assignments in Python, enabling students to implement these reasoning systems and apply them to real AI problems.
Lógica proposicional
Module 1 · 57 Minutes to complete
Lógica proposicional parte 2
Module 2 · 4 Hours to complete
Lógica temporal y Lógica de predicados
Module 3 · 1 Hours to complete
Teoría de la probabilidad
Module 4 · 4 Hours to complete
Teoría de la probabilidad (parte 2)
Module 5 · 4 Hours to complete
Teoría de la probabilidad (parte 3)
Module 6 · 4 Hours to complete
Fee Structure
Instructors
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.
Investigador de Carrera Titular B
David A. Rosenblueth is a researcher at the Universidad Nacional Autónoma de México (UNAM). He earned his Ph.D. from the University of Victoria in British Columbia, Canada. His research focuses on logical programming, model verification, and complex systems. Specifically, he has contributed to logical programming areas such as inference systems inspired by syntactic analyzers, inductive logic programming, program transformation, and logic programming applied to genetic regulation. In model verification, he has studied model updating and verification for hidden Markov models, multi-agent systems, genetic regulation, robotics, and embedded systems. Additionally, he has worked on self-organizing traffic light systems in complex systems. On Coursera, Rosenblueth teaches courses like "Inteligencia artificial: proyecto final" and "Razonamiento artificial," focusing on artificial intelligence and reasoning. His expertise spans various computational fields, making him a valuable instructor for students interested in AI and related technologies.
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4.1 course rating
105 ratings
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