teaching

Modern Statistics for Engineers

This course covers fundamentals of modern statistics and its applications in different engineering disciplines. The focus is on understanding and implementing different statistical concepts and utilizing Python programming to solve real-word problems. These include Probability, Probabilistic Models, Bayesian versus Frequentist Statistics, Density Estimation, Clustering, Classification, Kernel Methods, Gaussian Processes, Optimization, Error Estimation, Markov Processes, Monte Carlo and MCMC Inference.

Applied Probability and Statistics in CEE

In this course, we cover the fundamental concepts of probability and statistics. Topics include descriptive statistics for summarizing and visualizing data, probability theory including basic concepts and distributions, analysis of discrete and continuous random variables, joint distributions, sampling techniques, regression analysis, correlation, goodness-of-fit tests, and nonparametric methods. Throughout this course, I prioritize real-world applications, ensuring students develop practical problem-solving skills essential for success in all CEE disciplines.