Statistical inference is about drawing conclusions from data that involves randomness. In this course, you’ll learn these ideas from an engineering perspective. While statistics once focused on small agricultural or industrial experiments, today’s challenges are much larger, driven by the rise of computers and the information age. Modern problems involve storing, organizing, and analyzing massive datasets, leading to fields like data mining and bioinformatics. At its core, this is all about learning from data—finding patterns and insights in complex information.nThis growing demand has sparked a major shift in the statistical sciences, with strong contributions from computer science and engineering. As a result, students with skills in statistics, computing, or data science are highly valued in both industry and graduate studies. This background also opens doors to many other fields, including finance, economics, healthcare, and the social sciences.nIn this course, “inference” refers to methods that use observed data to estimate or predict unknown quantities, especially when some prior knowledge about the system is available. These methods often serve as benchmarks for modern learning algorithms. You’ll be introduced to key techniques such as Bayesian inference, maximum likelihood estimation, Kalman and particle filters, sampling methods, and models like hidden Markov models and Bayesian networks. Overall, the course builds a strong foundation for understanding and working with data in a wide range of real-world applications.
