Akademik Dersler - İçerik

Weeks	Topics
Week - 1	Course introduction, history of artificial neural networks, and their relation to biological neural systems.
Week - 2	Artificial neuron model, input-output relationships, and basic activation functions (sigmoid, tanh, ReLU).
Week - 3	Structure of single-layer and multi-layer perceptrons (MLP) and introduction to feedforward networks.
Week - 4	Forward propagation and backpropagation algorithms, and loss functions.
Week - 5	Gradient descent and optimization techniques; learning rate and epoch concepts.
Week - 6	Overfitting and regularization techniques, dropout, L1/L2 regularization.
Week - 7	Supervised learning applications: classification and regression problems.
Week - 8	Unsupervised learning and Kohonen networks (self-organizing maps).
Week - 9	Introduction to recurrent neural networks (RNN), applications in time series and sequences.
Week - 10	Hopfield networks and the basic principles of energy-based models.
Week - 11	Introduction to Boltzmann machines and restricted Boltzmann machines (RBM).
Week - 12	Fundamentals of convolutional neural networks (CNN) and applications in image processing.
Week - 13	Comparison of network architectures, advantages and disadvantages; real-world data applications.
Week - 14	Presentation of term projects, overall evaluation, and brief look at advanced topics.