Deep Autoencoders Lecture
Nowadays, Artificial Intelligence drives scientific and economic growth worldwide. This is largely due to advances in Machine Learning (ML), notably in Deep Neural Networks (DNNs), which are essentially massive ‘learning by experience/examples’ systems. Their applications span and revolutionize almost every human activity:
-Autonomous Systems (cars, drones, vessels),
-Media Content and Art Creation (including fake data creation/detection), Social Media Analytics,
-Medical Imaging and Diagnosis,
-Financial Engineering (forecasting and analytics), Big Data Analytics,
-Broadcasting, Internet and Communications,
-Robotics/Control
-Intelligent Human-Machine Interaction, Anthropocentric (human-centered)Computing,
-Smart Cities/Buildings and Assisted living.
-Scientific Modeling and Analytics.
Several DNN advances and challenges hit the news almost every day, arising discussions on AI ethics, privacy protection and its societal impact.
This lecture overviews Deep Autoencoders that has many applications in image denoising, classification, generation and in object pose estimation. It covers the following topics in detail: unsupervised learning, autoencoder principles, deep autoencoder types (Sparse/variational/ Convolutional/Adversarial Autoencoders) and their applications.