Probability and Statistical Data Analysis: Uncertainty Quantification and Data-Driven Decisions
Master the probabilistic reasoning and statistical inference that machine learning is built on. Part four in a five-part series, this online course teaches you how to quantify uncertainty and make data-driven decisions.
Course Information
Certificate Track
Learn for Free
About this Course
Study sampling distributions, the Central Limit Theorem, and asymptotic, bootstrap, and credible intervals to understand the reliability of estimates. Then connect uncertainty to real-world decisions with loss functions, human-in-the-loop rules, and utility-uncertainty tradeoffs, and evaluation practices that guard against failure modes like data leakage.
This course is part four of five in the Probability and Statistical Data Analysis Program, which includes the following:
- Foundations and Exploratory Data Analysis
- Discrete Distributions and Categorical Data
- Multivariate Statistics and Bayesian Inference
- Uncertainty Quantification and Data-Driven Decisions
- Hypothesis Testing and Machine Learning Model Validation
What you'll learn
- Apply sampling distributions and the Central Limit Theorem
- Construct confidence, bootstrap, and credible intervals
- Make data-driven decisions with loss functions
- Detect failure modes like data leakage
Meet your instructors
Yury Polyanskiy
Professor of Electrical Engineering and Computer Science
Yury Polyanskiy is the Cutten Professor of Electrical Engineering and Computer Science, a member of IDSS and LIDS at MIT, and an IEEE Fellow. Yury received Ph.D. degree in electrical engineering from Princeton University, Princeton, NJ in 2010. His research interests span information theory, machine learning and statistics. Dr. Polyanskiy won the 2020 IEEE Information Theory Society James Massey Award, 2013 NSF CAREER award and 2011 IEEE Information Theory Society Paper Award. Over the years, he worked on applied AI projects at Amazon and Anthropic.
