Neural Networks and Deep Learning

Catalog description

This course offers an in-depth study of neural network architectures and deep learning techniques that power today’s most advanced Artificial Intelligence systems. This course focuses specifically on the mathematical foundations, structural design, and training dynamics of neural networks. Topics include perceptrons, backpropagation, activation functions, optimization algorithms, regularization methods, and loss functions. Students will analyze and implement advanced architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Transformers, autoencoders, and generative models. The course also covers techniques for improving training performance, reducing overfitting, and interpreting model outputs. Through hands-on coding assignments, students will build and fine-tune deep models on real-world datasets. Emphasis is placed on critical model evaluation, explainability, and preparation for deployment in research and production contexts.