Mathematical and Computational Foundations of Artificial Intelligence

CS 311 is the math-and-stats backbone that makes the rest of Bilkent's AI track make sense: instead of treating models as black boxes, you derive why they work from linear algebra, probability, optimization, and statistical inference. Expect four projects where you implement regression, dimensionality reduction, sampling, and graphical-model inference from the ground up, alongside two quizzes and the usual midterm/final that test whether you can actually do the derivations. It sits between your earlier calc/linear algebra/probability courses and downstream electives like machine learning, computer vision, and NLP, so the cleaner your foundations here, the less you'll be guessing later.