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About the Programme Academic Staff Program Qualifications Lessons Matrix of Course & Program Qualifications Turkish Qualifications Framework (TQF) TQF & Program Qualifications
  1. Faculty of Computer and Information Sciences
  2. Department of Data Science and Analytics (English)
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  4. Machine Learning I: Supervised Learning I
  5. Description
  • Description
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  • ECTS Credit Load
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  • Learning Outcomes & Program Qualifications
Last Updated: 21/07/2026

Code - Course Title Course Type Laboratory + Practice ECTS
VBA200 - Machine Learning I: Supervised Learning I IV. SEMESTER 3 +1 6.0
Language of Instruction İngilizce
Course Type Required Courses
Course Instructor(s) ARAŞ. GÖR. DR. ÖNDER DORAK
Mode of Delivery Face to face
Place of Delivery
Used Educational Platforms
Prerequisites
Courses Recomended
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Work Placement
Aims of the Course

Weeks Learning Outcomes Topics Teaching Methods
Week - 1 Supervised learning fundamentals: Mathematical foundations
Week - 2 Model training and evaluation logic
Week - 3 Evaluation Metrics: Robust cross-validation strategies
Week - 4 ROC-AUC curves and Precision-recall trade-offs
Week - 5 Analyzing confusion matrices
Week - 6 Model Optimization: Hyperparameter tuning
Week - 7 Feature selection methods
Week - 8 Handling class imbalance
Week - 9 Diagnosing model bias and variance
Week - 10 Regression Models: Linear regression
Week - 11 Logistic regression applications
Week - 12 Classification Algorithms: Decision trees
Week - 13 Random forests and K-nearest neighbors
Week - 14 Support vector machines (SVM)

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

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