The course begins with an introduction to Artificial Intelligence (AI), presenting the concept of the Turing Test and introducing the most important AI systems in use today, including Large Language Models (LLMs) and generative AI systems for image and video creation. It then presents the main categories of AI, with particular emphasis on Machine Learning and its primary paradigms: supervised learning, unsupervised learning, and reinforcement learning.

The course continues with a more detailed examination of supervised learning, introducing the concepts of predictive models, model training, overfitting, and underfitting. It also presents the fundamental machine learning tasks of regression and classification. Students are then introduced to artificial neurons, artificial neural networks, and the backpropagation algorithm.

The course further explores deep learning methods and the concept of representation learning, highlighting how neural networks automatically learn meaningful feature representations from data. It subsequently covers unsupervised learning techniques, focusing on clustering algorithms, and provides an introduction to reinforcement learning, including the Q-learning algorithm.

In addition, the course provides an overview of modern neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), context modeling techniques, and Transformer architectures.

Finally, the course concludes with an overview of contemporary AI applications, with particular emphasis on communication and digital media.