Akademik Dersler - Tanıtım

Last Updated: 30/06/2026

Code - Course Title	Course Type	Laboratory + Practice	ECTS
BVA100 - Fundamentals of Machine Learning	II. SEMESTER	2 +2	5.0

Language of Instruction	Türkçe
Course Type	Required Courses
Course Instructor(s)	ÖĞR. GÖR. AHMET MÜCAHİD ARVASİ
Mode of Delivery	Face to face
Place of Delivery
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Prerequisites	There are no prerequisites or corequisites for this course.
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Aims of the Course

Weeks	Learning Outcomes	Topics	Teaching Methods
Week - 1		Introduction to Machine Learning: Basic Concepts, Differences from AI, and Learning Types
Week - 2		Data Preprocessing 1: Understanding Datasets, Missing Data Analysis, and Data Cleaning
Week - 3		Data Preprocessing 2: Feature Scaling, Categorical Data Encoding, and Splitting Datasets (Train/Test Split)
Week - 4		Supervised Learning 1: Algorithm Selection and Linear Regression Fundamentals
Week - 5		Supervised Learning 2: Classification Logic and Logistic Regression
Week - 6		Supervised Learning 3: Decision Trees and Random Forest Algorithms
Week - 7		Supervised Learning 4: Support Vector Machines (SVM) and the Kernel Trick
Week - 8		Midterm Exam
Week - 9		Model Evaluation and Metrics: Confusion Matrix, Precision, Recall, and F1-Score
Week - 10		Model Validation and Improvement: Cross-Validation and Hyperparameter Optimization (Grid Search, Random Search)
Week - 11		Unsupervised Learning 1: Clustering Fundamentals and K-Means Algorithm
Week - 12		Unsupervised Learning 2: Hierarchical Clustering and Using Dendrograms
Week - 13		Dimensionality Reduction Techniques: Principal Component Analysis (PCA) and Reducing Data Complexity
Week - 14		Machine Learning Project Lifecycle: End-to-End Model Design, Reporting Findings, and Project Presentations

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Assessment Method and Passing Requirements
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