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David A. Rosenblueth and Stalin Muñoz Gutiérrez

El razonamiento formal juega un papel importante en la inteligencia artificial. Hay dos maneras principales de formalizar razonamiento: una que enfatiza la deducción (lógica), y otra que enfatiza la incertidumbre (teoría de la probabilidad). En este curso vamos a cubrir una introducción tanto a la lógica (vamos a cubrir tres lógicas) como a la teoría de la probabilidad (vamos a cubrir tres modelos gráficos probabilísticos).

Algunas tareas requieren programación básica en Python: El alumno deberá completar código al que se le ha eliminado una parte.

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What's inside

Syllabus

Lógica proposicional
En este módulo de razonamiento lógico podrás familiarizarte con la lógica proposicional. Verás una primera manera de formalizar razonamiento y los problemas NP-completos, que son arquetípicos en inteligencia artificial.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Combines deductive and probabilistic reasoning, which broadens learners' perspective of formal reasoning
Introduces three distinct logics, providing a rounded understanding of logical reasoning and problem-solving
Provides a solid foundation in both logic and probability theory, making it suitable for learners interested in artificial intelligence
Requires basic programming skills in Python, which may be a barrier for some learners
Covers a wide range of topics within logic and probability, ensuring a comprehensive understanding of these fundamental concepts

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Reviews summary

Fundamentos teóricos sólidos en ia

Según los estudiantes, este curso proporciona una base teórica sólida y profunda en el razonamiento para inteligencia artificial, cubriendo lógica y probabilidad. Destacan la claridad de las explicaciones y la calidad de las clases impartidas por el instructor. Si bien la mayoría lo considera una excelente introducción conceptual, las tareas de programación en Python son consistentemente señaladas como desafiantes, especialmente para quienes tienen poca experiencia previa. Algunas partes del contenido son densas y requieren dedicación y esfuerzo. No es el curso ideal para quienes buscan aplicaciones prácticas inmediatas.
Algunos temas son complejos y requieren dedicación.
"la parte de procesos de decisión de Markov es bastante compleja."
"Algunos módulos son muy densos y tuve que ver los vídeos varias veces."
"La teoría excelente, la práctica frustrante."
"El material es profundo, pero a veces se siente abrumador."
La calidad de las clases y el instructor es alta.
"Las explicaciones son muy claras y el profesor domina el tema."
"Las explicaciones del Dr. son fantásticas."
"El profesor explica los temas complejos de forma muy clara."
Fundamentos esenciales en lógica y probabilidad.
"El curso es excelente para entender las bases teóricas del razonamiento en IA."
"Proporciona una base conceptual muy fuerte."
"Este curso me dio una base increíblemente fuerte en los principios de IA."
"Aprendí los conceptos lógicos y probabilísticos de manera profunda."
No ideal para aplicación práctica rápida.
"Demasiado teórico para mi gusto. Buscaba algo más práctico..."
"Se centra mucho en la formalización matemática y lógica."
"No lo encontré muy útil para mi objetivo de aplicar IA en proyectos rápidos."
Asignaciones de programación desafiantes.
"Las tareas de Python son demasiado difíciles para alguien con poca experiencia."
"Las tareas de programación ayudan a fijar conceptos, pero si no tienes experiencia en Python, prepárate para investigar por tu cuenta."
"Las tareas de código son retadoras pero muy formativas."
"Me costó trabajo completar algunas tareas de programación."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Razonamiento artificial with these activities:
Revisit logic basics
Prepare yourself for more advanced logic topics by brushing up on your basics.
Browse courses on Logic
Show steps
  • Review your notes or textbooks on propositional logic.
  • Go over practice problems and exercises to test your understanding.
Revise the basics of probability theory
Reviewing basic probability theory will strengthen your foundation for the more advanced concepts covered in this course.
Browse courses on Probability Theory
Show steps
  • Review the concepts of sample space, events, and probability.
  • Practice calculating probabilities using conditional probability and the multiplication rule.
  • Solve problems involving independent events.
Read 'Introduction to Logic' by Irving Copi
This book provides a comprehensive overview of logic, including both propositional and predicate logic. It will supplement the course material and enhance your understanding of the subject.
Show steps
  • Read the chapters on propositional logic and predicate logic.
  • Work through the exercises at the end of each chapter.
Ten other activities
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Show all 13 activities
Organize and review your course notes
Taking the time to organize and review your course notes will help you solidify your understanding of the material and identify areas that need further attention.
Show steps
  • Gather all of your course notes, including lecture notes, slides, and handouts.
  • Organize your notes by topic or module.
  • Review your notes regularly, especially before exams.
Share your knowledge by mentoring a peer
Deepen your understanding of the course material by helping others learn.
Show steps
  • Reach out to a peer who is struggling with the material or who would like additional support.
  • Review the course material together and answer any questions your peer may have.
  • Provide encouragement and support to your peer as they work through the course.
Solve practice problems on propositional logic
Solving practice problems on propositional logic will reinforce your understanding of logical reasoning and improve your problem-solving skills.
Browse courses on Propositional Logic
Show steps
  • Find a collection of practice problems on propositional logic.
  • Work through the problems step-by-step, using truth tables or other techniques to evaluate logical expressions.
  • Review your solutions and identify areas where you need improvement.
Solve probability problems
Sharpen your probability skills by working through a variety of problems.
Browse courses on Probability
Show steps
  • Find practice problems online or in textbooks.
  • Work through the problems step-by-step, checking your answers against the provided solutions.
Follow tutorials on Markov chains
Tutorials on Markov chains will provide hands-on experience and deepen your understanding of their properties and applications.
Browse courses on Markov Chains
Show steps
  • Find tutorials that cover the basics of Markov chains, such as state transitions and transition probabilities.
  • Work through examples to practice applying Markov chain concepts.
  • Explore more advanced tutorials on topics such as hidden Markov models.
Participate in a study group for temporal logic
Collaborating with peers in a study group will provide opportunities to discuss concepts, solve problems, and reinforce your understanding of temporal logic.
Show steps
  • Find a study group or create one with classmates.
  • Meet regularly to discuss the course material, work on assignments, and prepare for exams.
  • Take turns presenting concepts and leading discussions.
Design a logical circuit using propositional logic
Designing a logical circuit using propositional logic will demonstrate your understanding of how logical concepts can be applied in practical settings.
Browse courses on Propositional Logic
Show steps
  • Choose a simple logical function to implement, such as an AND gate or an OR gate.
  • Design the circuit using logic gates and wires.
  • Simulate the circuit to verify its functionality.
Explore advanced logic concepts
Expand your knowledge of logic by exploring more advanced concepts.
Show steps
  • Search for online tutorials on temporal logic, predicate logic, or modal logic.
  • Follow the tutorials, taking notes and completing any practice exercises.
Build a logic-based decision-making system
Apply your knowledge of logic to create a practical decision-making system.
Show steps
  • Define the problem you want to solve and the decisions that need to be made.
  • Design a logic-based model to represent the problem and the possible outcomes.
  • Implement your model using a logic programming language or a general-purpose programming language with logic libraries.
  • Test and evaluate your system to ensure its accuracy and efficiency.
Develop a probabilistic model for a real-world scenario
Demonstrate your understanding of probabilistic models by applying them to a real-world problem.
Show steps
  • Identify a real-world scenario that can be modeled probabilistically.
  • Formulate a probabilistic model to represent the scenario, including the relevant variables and their relationships.
  • Collect data and use it to estimate the parameters of your model.
  • Validate your model by testing its predictions against real-world observations.
  • Document your model and its results in a technical report or presentation.

Career center

Learners who complete Razonamiento artificial will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer designs, builds, and deploys machine learning models. This course may be helpful in developing skills in logic and probability, which are foundational in machine learning. This course may be especially helpful for those who wish to specialize in logical or probabilistic machine learning.
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, builds, and deploys artificial intelligence solutions. This course may help build a foundation in artificial intelligence by developing skills in logic and probability. These skills are foundational in artificial intelligence. This course may be especially helpful for those who wish to specialize in logical or probabilistic artificial intelligence.
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical methods to solve complex problems in business and industry. This course may be helpful in developing skills in logic and probability, which are used in operations research. This course may be especially helpful for those who wish to specialize in logical or probabilistic operations research.
Business Analyst
A Business Analyst uses data and analytical methods to help businesses make better decisions. This course may be helpful in developing skills in logic and probability, which are used in business analysis. This course may be especially helpful for those who wish to specialize in logical or probabilistic business analysis.
Financial Analyst
A Financial Analyst uses data and analytical methods to help businesses make better financial decisions. This course may be helpful in developing skills in logic and probability, which are used in financial analysis. This course may be especially helpful for those who wish to specialize in logical or probabilistic financial analysis.
Quantitative Analyst
A Quantitative Analyst uses data and analytical methods to help businesses make better investment decisions. This course may be helpful in developing skills in logic and probability, which are used in quantitative analysis. This course may be especially helpful for those who wish to specialize in logical or probabilistic quantitative analysis.
Actuary
An Actuary uses mathematical and statistical methods to assess risk and uncertainty. This course may be helpful in developing skills in logic and probability, which are used in actuarial science. This course may be especially helpful for those who wish to specialize in logical or probabilistic actuarial science.
Statistician
A Statistician collects, analyzes, and interprets data to extract meaningful insights. This course may be helpful in developing skills in logic and probability, which are used in statistics. This course may be especially helpful for those who wish to specialize in logical or probabilistic statistics.
Economist
An Economist uses data and analytical methods to study the economy and make predictions about its future performance. This course may be helpful in developing skills in logic and probability, which are used in economics. This course may be especially helpful for those who wish to specialize in logical or probabilistic economics.
Sociologist
A Sociologist studies human society and social behavior. This course may be helpful in developing skills in logic and probability, which are used in sociology. This course may be especially helpful for those who wish to specialize in logical or probabilistic sociology.
Political Scientist
A Political Scientist studies politics and government. This course may be helpful in developing skills in logic and probability, which are used in political science. This course may be especially helpful for those who wish to specialize in logical or probabilistic political science.
Psychologist
A Psychologist studies the human mind and behavior. This course may be helpful in developing skills in logic and probability, which are used in psychology. This course may be especially helpful for those who wish to specialize in logical or probabilistic psychology.
Neuroscientist
A Neuroscientist studies the brain and nervous system. This course may be helpful in developing skills in logic and probability, which are used in neuroscience. This course may be especially helpful for those who wish to specialize in logical or probabilistic neuroscience.
Data Scientist
A Data Scientist collects, analyzes, and interprets data to extract meaningful insights. This course may be helpful in developing skills in logic and probability, which are used in data analysis and interpretation. This course may be especially helpful for those who wish to specialize in probabilistic data science.
Software Engineer
A Software Engineer designs, builds, and deploys software applications. This course may be helpful in developing skills in logic and probability, which are used in software design and development. This course may be especially helpful for those who wish to specialize in logical or probabilistic software engineering.

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Razonamiento artificial.
Este libro proporciona una visión general completa de la inteligencia artificial, incluidos el razonamiento lógico y probabilístico. Es una valiosa referencia para estudiantes y profesionales.
Este libro proporciona una visión general completa de la inteligencia artificial, con un enfoque en métodos modernos. Es una valiosa referencia para estudiantes y profesionales interesados en el desarrollo de sistemas inteligentes.
Ofrece una visión general integral del campo de la inteligencia artificial, incluyendo técnicas de razonamiento lógico y probabilístico tratadas en el curso.
Este libro proporciona una introducción a la probabilidad y la estadística, con un enfoque en aplicaciones en ingeniería y ciencias. Es una buena opción para estudiantes que desean aprender los conceptos básicos de probabilidad y estadística.
Este libro proporciona una introducción a la probabilidad y la estadística, con un enfoque en aplicaciones en ingeniería y ciencias. Es una buena opción para estudiantes que desean aprender los conceptos básicos de probabilidad y estadística.
Este libro proporciona una introducción a la teoría de la probabilidad y la estadística, con un enfoque en aplicaciones en ingeniería y ciencias. Es una buena opción para estudiantes que desean aprender los conceptos básicos de teoría de la probabilidad y estadística.
Este libro proporciona una introducción a la teoría de la decisión, con un enfoque en aplicaciones en ingeniería y ciencias. Es una buena opción para estudiantes que desean aprender los conceptos básicos de teoría de la decisión.
Provides a clear and concise introduction to logic, with a focus on applications in computer science. It good choice for students who want to learn the basics of logic.
Provides a comprehensive introduction to probability and statistics, with a focus on applications in computer science. It valuable resource for students who want to learn the basics of probability and statistics.
Provides a comprehensive introduction to Bayesian reasoning and machine learning. It valuable resource for students who want to learn the basics of Bayesian reasoning and machine learning.

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