Module Handbook

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Subject name: Probality and statistics - Back

Author: P.Dalaijargal

Topic Content:

Course content:

This course covers the basic elements of probabilistic models, including discrete and continuous distributions, multiple random variables, means and variances, conditioning, Bayes formulas, and limit theorems. It continues on functions of random variables, and a deeper view of conditional expectations. A thorough introduction into Bayesian inference in discrete continuous, and mixed settings (posterior distributions, maximum a posteriori probability estimation, linear and general least mean squares estimation, Beta distributions, linear normal models). An introduction to stochastic processes (probabilistic models that evolve in time) focused on the Bernoulli and Poisson processes and finite-state Markov chains. As such, it provides a solid foundation for taking other classes that rely on probabilistic reasoning such as machine learning, natural language processing, computational biology and bioinformatics, computer vision.      

Learning outcome:

 The basic structure and elements of probabilistic models

 Random variables, their distributions, means, and variances

 Probabilistic calculations

 Inference methods

 Laws of large numbers and their applications

 Random processes