Akademik Dersler - Tanıtım

Last Updated: 22/06/2026

Code - Course Title	Course Type	Laboratory + Practice	ECTS
BVA222 - Artificial Neural Networks	IV. SEMESTER	2 +2	5.0

Language of Instruction	Türkçe
Course Type	Area Elective 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.
Courses Recomended
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Work Placement
Aims of the Course

Weeks	Learning Outcomes	Topics	Teaching Methods
Week - 1		Biological and Artificial Neurons, Perceptrons, and Introduction to Neural Networks
Week - 2		Multi-Layer Networks (MLP) and Feedforward Computation
Week - 3		Activation Functions (Sigmoid, ReLU, Tanh, Softmax) and Loss Functions
Week - 4		Backpropagation Algorithm and the Mathematics of Gradient Descent
Week - 5		Advanced Optimization Techniques: Adam, RMSprop, and Momentum
Week - 6		Preventing Overfitting: Regularization, Early Stopping, Dropout, and Batch Normalization
Week - 7		Introduction to Convolutional Neural Networks (CNN): Filtering, Pooling, and Feature Extraction
Week - 8		Midterm Exam
Week - 9		Advanced CNN Architectures (ResNet, VGG) and Image Classification Applications
Week - 10		Introduction to Recurrent Neural Networks (RNN) and Sequence Modeling
Week - 11		Advanced Recurrent Networks: LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) Architectures
Week - 12		Model Training on Large Datasets, Transfer Learning, and Fine-Tuning
Week - 13		Hyperparameter Optimization, Model Evaluation Metrics, and Cross-Validation
Week - 14		Real-World Big Data Analytics Projects and Student Presentations

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Assessment Method and Passing Requirements
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