The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.
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)
The contents of this course are heavily based upon the corresponding MIT class -- Introduction to Probability -- a course that has been offered and continuously refined over more than 50 years. It is a challenging class but will enable you to apply the tools of probability theory to real-world applications or to your research.
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/.
John Tsitsiklis, Patrick Jaillet, Qing He, Jimmy Li, Jagdish Ramakrishnan, Katie Szeto, Kuang Xu, Dimitri Bertsekas and Eren Can Kizildag
Arnaud is taking this course right now, spending 4 hours a week on it and found the course difficulty to be medium.
This is a great introducing course on probability. A certain level in math is a prerequisite, but nothing complicated. The teacher is clear and the his explanations really help to understand notion that can appear complicated at first glance. The exercices are designed to help the understanding. They're not "challenging", but are helpful.
Federico is taking this course right now, spending 12 hours a week on it and found the course difficulty to be hard.
It is an introductory course but, I would not recommend it to somebody that doesn't have any idea of probability or statistics. The problems and exams have some difficult exercises. After taking many probability courses, this is the first time I feel I really understand it. It is fun and rigorous at the same. It is one of the best MOOC I've taken if not the best.