Beginning with fundamental counting principles and Bayesian reasoning, this course guides students in modeling problems in computer science using random variables and probability distributions. It then develops the laws of large numbers and the central limit theorem to reveal the regularities behind large-scale data sampling. Finally, the course connects these foundations to maximum likelihood estimation and regression analysis, establishing core competencies for machine learning and statistical inference.