Sequential decision-making under uncertainty is the central question here: how should an agent act when outcomes are stochastic and the world's dynamics are only partially known? You'll move from the clean Markov decision process world, where Bellman equations and dynamic programming give you exact answers, into the messier model-free regime of Q-learning, temporal difference, and policy gradients, with multi-armed bandits framing the exploration-exploitation tradeoff. Two quizzes, a midterm, a final, and an implementation project anchor the work; the math leans on probability and linear algebra, and what you build here is the foundation for control, robotics, and modern ML research.
→ STARS müfredatı (resmi syllabus)
Bilkent'in resmî syllabus'ünden. Sağdaki etiket o çıktının hangi değerlendirmeyle ölçüldüğünü söylüyor.
You should try to solve the assignments given here by yourself. Discussion of the assignments with other students and online tools (e.g., ChatGPT) are allowed and encouraged. However, the final submitted work must be your own. You should not submit anything that you do not understand. We may invite you to explain your solutions at a face-to-face (or Zoom) meeting with the instructor and the grader; at the end of this interview, you may get no credit for the assignment if it is deemed that you ha
İlk dosyayı sen atarsan: not, slayt, geçmiş sınav, çözüm, cheat-sheet, ne varsa. defter ekibi öğrenci paylaşımlarından bu dersin notlarını yazar. Drive linki / PDF / ZIP, hepsi olur.
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