Specialization in Advanced Artificial Intelligence

Course Content:

Fundamentals of Advanced Artificial Intelligence:
Review of key concepts in AI and machine learning.
Deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN) architectures.
Introduction to development frameworks such as TensorFlow, PyTorch, and Keras.

Deep Neural Networks and Complex Architectures:
Design and training of deep neural networks for complex classification and regression tasks.
Implementation of advanced architectures such as ResNet, Inception and GANs (Generative Adversarial Networks).
Regularization and optimization techniques to improve model performance.

Reinforcement Learning:
Basic concepts of Reinforcement Learning and its application in autonomous decision making.
Implementation of algorithms such as Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO).
Use cases in robotics, gaming, and recommender systems.

Generative Models and GANs:
In-depth study of the design and implementation of generative adversarial networks (GANs).
Advanced applications of GANs in the creation of images, text, and music.
Techniques for training stability and improvements in the quality of generations.

Advanced Natural Language Processing (NLP):
Advanced techniques in NLP: Transformers, BERT, GPT, and other language models.
Application of NLP in chatbots, sentiment analysis, and machine translation.
Development of language models and their integration in conversational AI applications.

Computer Vision:
Implementation of advanced computer vision techniques for object recognition, segmentation, and face detection.
Use of CNNs in image classification, anomaly detection, and video analysis tasks.
Industrial applications in security, automotive and manufacturing.

Explainable AI systems and AI ethics:
Developing AI models that are interpretable and transparent.
Techniques for making AI systems understandable to humans, including LIME and SHAP.
Ethical considerations in advanced AI development: bias, privacy, and liability.

Optimization and Scalability of AI Systems:
Strategies for optimizing AI models for large data volumes and high availability.
Implementation of AI in production environments: DevOps for AI, and use of cloud computing.
Scalability and performance: techniques to improve computational efficiency.

Final Specialization Project:
Participants will develop a complete advanced AI project, applying all the knowledge acquired.
The project will include the definition of a complex problem, model design, implementation and performance evaluation.
Presentation of the project to a panel of AI experts, followed by detailed analysis and recommendations for improvement.

Additional Benefits:

Advanced Artificial Intelligence Certification:
Upon completion of this course, you will receive a prestigious certification that endorses your skills in designing and implementing advanced AI solutions, positioning you as a leader in the technology industry.

Access to Exclusive Resources and Cutting-Edge Software:
During the course, you will have access to software licenses, exclusive datasets, and educational resources that will allow you to practice and hone your skills.

Expert Mentoring and Global Collaboration Opportunities:
Connect with AI experts from around the world, participate in one-on-one mentoring sessions, and collaborate on high-impact international projects.

Course Duration: 12 months

Modality: 100% online, with live classes, virtual labs, and access to recorded content.