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This course provides a comprehensive introduction to probabilistic modeling techniques and their applications in data science. Learn fundamental concepts of probability theory, statistical inference, and how to build mathematical models that incorporate uncertainty and randomness. Explore various probabilistic frameworks including Bayesian methods, graphical models, and machine learning approaches that rely on probabilistic foundations. Master techniques for parameter estimation, model selection, and uncertainty quantification through hands-on examples and theoretical foundations. Develop skills in implementing probabilistic algorithms and understanding their computational complexity. Gain expertise in applying probabilistic models to real-world data science problems including classification, regression, clustering, and prediction tasks. The curriculum covers both discrete and continuous probability distributions, conditional probability, Markov models, and advanced topics in probabilistic inference that are essential for modern data analysis and machine learning applications.
