Probability and statistics review, estimation (maximum likelihood, maximum a posterior), loss functions, model selection, feature representation, feature selection, naive Bayes, linear discriminant analysis, logistic regression, k-nearest neighbor, support vector machines, deep learning, linear regression, decision trees, ensemble methods (bagging, random forest, boosting) and clustering.
İlk dosyayı sen ekleyebilirsin — notlar, geçmiş finaller, çözümler, cheat-sheet, ne varsa. Drive linki / PDF / ZIP / fotoğraf, hepsi olur.
Şu an: mail at, ben düzenleyip yayına alayım. Form/upload UX yakında geliyor (Kimya tasarlıyor).
| Dönem | Course CPA | |
|---|---|---|
| 2025-2026 Fall | 2.62 | 2 sec · 133 öğr |
| 2024-2025 Fall | 2.43 | 2 sec · 130 öğr |
| 2024-2025 Spring | 2.51 | 2 sec · 130 öğr |
| 2023-2024 Fall | 2.49 | 2 sec · 138 öğr |
| 2023-2024 Spring | 2.44 | 1 sec · 65 öğr |
| 2022-2023 Fall | 2.57 | 2 sec · 137 öğr |
| 2022-2023 Spring | 2.60 | 1 sec · 65 öğr |
| 2021-2022 Fall | 2.32 | 2 sec · 125 öğr |
| 2021-2022 Spring | 2.43 | 1 sec · 65 öğr |
| 2020-2021 Fall | 2.44 | 2 sec · 128 öğr |
Aggregate course GPA — Bilkent STARS'tan public data. Hoca-bazlı per-section detayı için STARS evaluation report →. Öğrenci anket cevapları KVKK kapsamında defter'de tutulmaz.
Students must get at least 30/100 from the midterm to qualify for the final exam.
We follow the Generative AI policy guideline of Bilkent University which can be found here: https://w3.bilkent.edu.tr/bilkent/generative-artificial-intelligence-genai-guideline/
Introduction to Machine Learning Probability/Statistics Review and Estimation Naive Bayes Classifier Feature Selection Feature Extraction and Performance Metrics Google Colab Tutorial Linear Regression Logistic Regression SVM Neural Networks Progress Presentations and PyTorch Tutorial Decision Trees Ensemble Learning Clustering and Project Presentations ECTS - Workload Table: Activities Number Hours Workload Preparation for Midterm exam 1 20 20 Course hours 14 3 42 Homework 3 15 45 Midterm exam 1 2 2 Project (including preparation and presentation if applicable) 1 20 20 Report (including preparation and presentation if applicable) 1 3 3 Preparation for Final exam 1 20 20 Final exam 1 2 2 Total Workload: 154 Total Workload / 30: 154 / 30 5.13 ECTS Credits of the Course: 5 Type of Course: Independent Studies - Lecture - Practical - Project - Tutorial Course Material: Multimedia - OHP - PC - PP - Written Teaching Methods: Assignment - Exercises - Independent study - Lecture - Presentations - Training tutorial