Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty Of Computer And Information Technologies
  • Artificial Intelligence And Machine Learning Pr. (English)
  • Course Structure Diagram with Credits
  • Artificial Neural Networks
  • Learning Outcomes & Program Qualifications
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load
0 : Does not support   1 : Low-level support   2 : Mid-level support   3 : Top level support
Learning Outcomes123456789101112
Can mathematically explain an artificial neuron and different activation functions (Sigmoid, ReLU).132201321212
Can explain the gradient (derivative)-based optimization logic behind the Backpropagation algorithm.133211330110
Can build, train, and evaluate a Multilayer Perceptron (MLP) model using TensorFlow/Keras.322313322212
Can apply optimization (SGD, Adam) and regularization (Dropout) techniques in deep networks.322313322212
Can explain the purpose of a Convolutional Neural Network (CNN) layer (Filter, Stride, Padding).132212322212
Can explain the basic working principle of a Recurrent Neural Network (RNN) cell (LSTM/GRU) and why it is used for sequential data.123212332112
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  • PhD / Proficiency in Art
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