About Course
The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modelling 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.
Course Content
Module 0: Career Development
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Career Assessment
00:00
Module 1: Intro to Course
Module 2: Course Assessment
Module 3: Certification and Ranking
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