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Razonamiento artificial
This course is part of Conceptos Básicos de Inteligencia Artificial.
Course Cost
Free course
Intermediate
Skill Level
18 Hours
Self-paced lessons
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.
8,681 Enrolled
Spanish
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
Develop Markov Decision Process models for decision-making in uncertain environments
Apply game theory principles to multi-agent reasoning scenarios
Skills you'll gain
This course includes:
1.9 Hours PreRecorded video
7 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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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
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: Conceptos Básicos de Inteligencia Artificial
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Frequently asked Questions
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