Foundations of Artificial Neural Networks

Artificial Neural Networks (ANN) are at the core of modern Artificial Intelligence systems, powering applications ranging from intelligent automation to personalized recommendation platforms. Foundations of Artificial Neural Networks is designed to provide a comprehensive and structured understanding of neural network principles, architectures, and intelligent algorithms.
The course begins with a foundational overview of Artificial Intelligence and its evolution, setting the context for neural network development. You will then explore the core components of ANN, including biological inspiration, neuron models, layers, weights, bias, and activation functions. The architecture of neural networks is explained in a clear and systematic manner, enabling learners to understand how information flows across layers.
A dedicated section focuses on the backpropagation algorithm, explaining how errors are calculated and propagated backward through the network to adjust weights and improve performance. The course emphasizes conceptual clarity over unnecessary complexity, ensuring strong theoretical understanding.
In addition, you will be introduced to intelligent algorithms and the fundamental principles of recommender systems. These systems play a critical role in personalized applications such as e-commerce platforms, digital media services, and online content delivery.
This course is ideal for computer science students, AI enthusiasts, researchers, and professionals who wish to build a solid academic foundation in neural networks before progressing to advanced deep learning topics.
By the end of this course, learners will have a confident understanding of ANN architecture, learning mechanisms, and intelligent system design, preparing them for further exploration in advanced AI and research domains.