Artificial Intelligence and Deep Learning

PROGRAMING IN PYTHON

• Arrays, matrices and vectors
• Graphics
• Program flow management
• Interfaces and data loading
• Programming exercises

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

• History and Evolution of Artificial Intelligence
• Supervised, unsupervised and reinforced Learning
• Foundations of Machine Learning
• Machine Learning Paradigms
• Extensions

DEEP LEARNING

• Introduction to Deep Learning
• Feed-Forward single-layer networks
• Multilayer Networks
• Hyper-parameters and learning strategies
• Applications in Keras

COMPUTER VISION

• Introduction to CNN
• CNN architectures
• Object detection and semantic segmentation
• CNN for image generation

SEQUENTIAL NETWORKS AND NATURAL LANGUAGE PROCESSING

• Sequential and time series problems
• Recurrent networks
• LSTM and GRU models
• Attention models and Transformers
• Applications to Natural Language Processing

GENETIC ALGORITHMS AND EVOLUTIONARY COMPUTATION

• Introduction to Evolutionary Computation
• Programming an Evolutionary Algorithm
• Introduction to Genetic Programming
• Genetic Programming: Finding the Hidden Function
• Evolutionary Machine Learning

UNSUPERVISED AND REINFORCED LEARNING

• Introductions to unsupervised learning
• Association rules and Recommendation systems
• Advanced Clustering
• Introduction to reinforcement learning
• Markov decision process
• OpenAI GYM

AUGMENTED INTELLIGENCE AND HUMAN MACHINE INTERACTION

• Cognitive Theories
• Interaction design
• Data and AI Ethics

SEMINARS

• Seminars on applications of Deep Learning to the fields of medicine, finance, automotive driving, computer vision, speech recognition and others

MASTER’S THESIS