EENG 582 Artificial Neural Networks
Neural Network Concepts: what is a neural network? biological neuron, artificial neuron, neural network topologies. Learning in Neural Networks: types of learning, learning rules, error correction learning, Hebbian learning, competitive learning, Boltzmann learning. Application Tasks: function approximation, classification, association, application examples. Feedforward Networks: perceptron, multi-layer perceptron, radial basis function network, self-organizing map. Feedback Networks: Hopfield network, Boltzmann machine, real-time recurrent network.
EENG 582 Yapay Sinir Ağları
Sinir Ağı Kavramları: Sinir ağı nedir? biyolojik nöron, yapay nöron, sinir ağı topolojileri. Sinir Ağlarında Öğrenme: öğrenme türleri, öğrenme kuralları, hata düzeltmeli öğrenme, Hebbian öğrenme, rekabetçi öğrenme, Boltzmann öğrenme. Uygulama Görevleri: fonksiyon yaklaşımı, sınıflandırma, ilişkilendirme, uygulama örnekleri. İleri Besleme Ağları: algılayıcı, çok katmanlı algılayıcı, radyal tabanlı işlev ağı, kendi kendini organize eden harita. Geri Besleme Ağları: Hopfield ağı, Boltzmann makinesi, gerçek zamanlı tekrarlayan ağ.
- Instructor: Hasan Amca
Continuous-time and discrete-time signals and systems. Linear time-invariant (LTI) systems: system properties, convolution sum and the convolution integral representation, system properties, LTI systems described by differential and difference equations. Fourier series: Representation of periodic continuous-time and discrete-time signals and filtering. Continuous time Fourier transform and its properties: Time and frequency shifting, conjugation, differentiation and integration, scaling, convolution, and the Parseval’s relation. Representation of aperiodic signals and the Discrete-time Fourier transform. Properties of the discrete-time Fourier transform.
Storage structures and memory allocations. Primitive data structures. Data abstraction and Abstract Data Types. Array and record structures. Sorting algorithms and quick sort. Linear & binary search. Complexity of algorithms. String processing. Stacks & queues; stack operations, implementation of recursion, polish notation and arithmetic expressions. Queues and their implementations. Dequeues & priority queues. Linked storage representation and linked-lists. Doubly linked lists and circular lists. Binary trees. Tree traversal algorithms. Tree searching. General trees. Graphs; terminology, Operation on graphs and traversing algorithms. (Prerequisite: EENG112)
In partial fulfillment of graduation requirements, each student is required to complete 40 working days of training during the summer vacations, normally at the end of the junior year, in accordance with rules and regulations set by the Department. Summer training involves full-time work experience in industry in the area of student career interest. A formal report and evaluation by work supervisor required. Prerequisite: Junior standing and consent of department