Probability and Statistical Data Analysis: Multivariate Statistics and Bayesian Inference
Master the probabilistic reasoning and statistical inference that machine learning is built on. Part three in a five-part series, this online course introduces multivariate statistics and Bayesian inference.
Course Information
Certificate Track
Learn for Free
About this Course
Model continuous data with feature vectors, covariance, and multivariate normal conditionals to understand how variables relate and make predictions. Then apply the same mathematical framework to Bayesian inference, building the foundations for prediction and posterior inference.
This course is part three 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
- Measure relationships with covariance and correlation
- Predict variables using multivariate normal conditionals
- Perform Bayesian inference on model parameters
- Compute posterior distributions from data
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.
