Fundamentals of Neural Networks: The course introduces the basic principles of artificial neural networks. The terminology and key building block methods of artificial neural networks are studied. The course starts with an introduction perceptrons and perceptron learning algorithm. Then, multilayer perceptrons (MLP) are analyzed in details together with their learning algorithm which is backpropagation algorithm. After that, the course covers two other network models that are Hopfield model and Kohonen's model. Kohonen's model is a model developed under the architecture of self organized map (SOM). Finally, the course studies on recent models, including recurrent neural networks (RNN) and convolutional neural networks (CNN). several applications such as image retrieval and classification. The emphasis will be on MLP and simulations of all the models.