Antes de comenzar
Aprender en edX
En este curso con ritmo de aprendizaje definido por el instructor, debes completar el curso en el plazo definido.
Lo que aprenderás
- The basic structure and elements of probabilistic models
- Random variables, their distributions, means, and variances
- Probabilistic calculations
- Inference methods
- Laws of large numbers and their applications
- Random processes
Probabilistic models use the language of mathematics. But instead of relying on the traditional "theorem-proof" format, we develop the material in an intuitive -- but still rigorous and mathematically-precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.
The course covers all of the basic probability concepts, including:
- multiple discrete or continuous random variables, expectations, and conditional distributions
- laws of large numbers
- the main tools of Bayesian inference methods
- an introduction to random processes (Poisson processes and Markov chains)
This course is part of the MITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.
Conoce a tus instructores
Testimonios de los estudiantes
"You won’t find another intro to probability with greater depth and breadth."
"This is a great course for those serious about forming a solid foundation in probability."
"[This course] has created a love for probabilistic models, that, I guess, truly govern everything around us."
"This should be in top 10 MOOCs of all time."
The material covered, and the resources (videos, etc.) are largely the same, but homeworks and exams contain revised and new problems.
What textbook do I need for the course?
None - there is no required textbook. The class follows closely the text Introduction to Probability, 2nd edition, by Bertsekas and Tsitsiklis, Athena Scientific, 2008. (See the publisher's website or Amazon.com for more information.) However, while this textbook is recommended as supplemental reading, the materials provided by this course are self-contained.
What is the format of the class?
The course material is organized along units, each unit containing between one and three lecture sequences. (For those who purchase the textbook, each unit corresponds to a chapter.) Each lecture sequence consists of short video clips, interwoven with short problems to test your understanding. Each unit also contains a wealth of supplementary material, including videos that go through the solutions to various problems.
How much do I need to work for this class?
This is an ambitious class in that it covers a lot of material in substantial depth. In addition, MIT considers that the best way to master the subject is by actually solving on your own a fair number of problems. MIT students who take the corresponding residential class typically report an average of 11-12 hours spent each week, including lectures, recitations, readings, homework, and exams.
¿Quién puede hacer este curso?
Lamentablemente, las personas de uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.
Statistics and Data Science Programa MicroMasters® de MITx
Obtén un Certificado de Programa MicroMasters® de 1 año si tomas un curso a la vez.Ver el programa
- 130–182 horas de trabajo
Learn the methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest, and then assess that knowledge— Course 2 of 4 in the MITx MicroMasters program in Statistics and Data Science.
- 160–224 horas de trabajo
Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. — Course 3 of 4 in the MITx MicroMasters program in Statistics and Data Science.
- Probability - The Science of Uncertainty and Data
- 130–182 horas de trabajo
An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. — Course 4 of 4 in the MITx MicroMasters program in Statistics and Data Science.
- 20–28 horas de trabajo
Solidify and demonstrate your knowledge and abilities in probability, data analysis, statistics, and machine learning in this culminating assessment. — Final Requirement of the MITx MicroMasters Program in Statistics and Data Science.
Comienza con análisis de datos y estadísticasExplorar 200 cursos de análisis de datos y estadísticas