Probability and Statistical Data Analysis

Machine learning (ML) runs on uncertainty. Every model makes probabilistic assumptions, every evaluation is a statistical inference, and every deployment is a choice among imperfect models.

• This series of online courses from the MIT Electrical Engineering & Computer Science Department teaches you how to work with uncertainty when applying ML methods so you understand not just how they work but why. You will learn to rigorously evaluate ML models, compare their performance, and confidently deploy the right models for their specific applications.
• We develop the material intuitively but with full mathematical precision, and in contact with real data. You’ll explore practical applications ranging from communication noise to scientific measurement. Every short course includes hands-on computational work, building your fluency with core Python ML libraries as you progress through the sequence.
• By the end of this series, you will be able to reason about uncertainty from raw data to validated, deployable decisions.
• For those who build and those who investigate, this series of online courses will benefit anyone who wants to master the probability and statistical inference underlying machine learning.

How is this series different from Probability - The Science of Uncertainty and Data (6.431x)?

• Content: The two offerings share a probability foundation, then go in different directions. 6.431x continues into stochastic processes, such as Bernoulli and Poisson processes, and Markov chains. This series turns toward statistics and applied data analysis: estimation, hypothesis testing, regression, and model validation.
• Format: This modular series is comprised of five short courses that are self-paced.
• Assessments: This series is assessed through Python labs and a final project, while 6.431x has timed exams.