Akademik Dersler - Tanıtım


Code - Course Title	YZM3001 - Fundamentals of Supervised Learning
Course Type	Required Courses
Language of Instruction	İngilizce
Laboratory + Practice	2+2
ECTS	5.0
Course Instructor(s)	DOKTOR ÖĞRETİM ÜYESİ NİHAL İNCE
Mode of Delivery	This course is offered exclusively in an in-person format.
Prerequisites	There are no prerequisites or co-requisites for this course.
Courses Recomended	There are no other recommended courses related to this one.
Required or Recommended Resources	James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: with applications in Python. Springer. Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly Media. Bishop, C. M., & Bishop, H. A. (2024). Pattern recognition and machine learning (2nd ed.). Springer.
Recommended Reading List	James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: with applications in Python. Springer. Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly Media. Bishop, C. M., & Bishop, H. A. (2024). Pattern recognition and machine learning (2nd ed.). Springer.
Assessment methods and criteria	Midterm Exam, Project, Final Exam
Work Placement	This is not suitable for this class.
Sustainability Development Goals

Weeks	Topics
Week - 1	The Supervised Learning Pipeline. Introduction to the Scikit-learn Library.
Week - 2	Linear Regression and Logistic Regression. Model Evaluation: Classification Metrics (Accuracy, Confusion Matrix).
Week - 3	Precision, Recall, F1-Score, ROC Curve and AUC.
Week - 4	Overfitting and the Bias-Variance Tradeoff.
Week - 5	Preventing Overfitting: Regularization - Ridge (L2) and Lasso (L1).
Week - 6	Model Validation: k-Fold Cross-Validation. Hyperparameter Tuning (Grid Search).
Week - 7	k-Nearest Neighbors (k-NN) Algorithm (Classification and Regression).
Week - 8	Support Vector Machines (SVM) I: Maximal Margin Classifier and Support Vectors.
Week - 9	The Kernel Trick - Polynomial and RBF Kernels.
Week - 10	Decision Trees: Structure, Entropy, Information Gain (ID3/C4.5).
Week - 11	Pruning, Classification and Regression Trees (CART).
Week - 12	Ensemble Methods: Bagging.
Week - 13	Ensemble Methods: Boosting. AdaBoost and Gradient Boosting.
Week - 14	Naive Bayes Classifier. Model Selection and Comparison.

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Toplam (%)		0