In this course Artificial Neural Network (ANN), which is a mathematical paradigm imitating biological neural network for reasoning
and problem solving is covered. With focus on practice, the course cover ANN models for various real-world applications and offers a
hands-on introduction to deep learning tools and techniques. Students do not need to have an extensive math background to
understand this course. The course covers McCulloch-Pitts model and basic neural network models, multilayer perceptron,
associative memory, self-organizing feature maps, recurrent neural networks, etc. and reviews applications of these models to
various types of data. Upon completion of this course, students will gain a broad understanding of the context of neural networks
and deep learning, the data demands of deep learning and the parameters for neural networks.