Probability and Statistical Data Analysis: Foundations and Exploratory Data Analysis
Master the probabilistic reasoning and statistical inference that machine learning is built on. Part two in a five-part series, this online course teaches discrete modeling skills that form the foundation of classification, prediction, and data analysis.
Course Information
Certificate Track
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About this Course
Learn how to model count data and categorical outcomes using probability distributions, contingency tables, and discrete statistical models. Quantify uncertainty with probability mass functions, and Binomial and Multinomial models. Gain the discrete modeling skills used in classification, machine learning, and statistical inference.
This course is part two 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
- Model count data with Binomial and Multinomial distributions
- Analyze categorical data with contingency tables
- Quantify uncertainty in proportions with PMFs
- Build the discrete foundations of classification
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.
