Course teacher

Associate teachers

-

ECTS credits

5

Number of hours: Lectures + Seminars + Exercises

30 / 0 / 6

Course objectives

Acquire the basics of fully connected, convolutional, recurrent and generative deep models and master their application in practice.

Through numerous successful applications, the mentioned techniques have demonstrated a successful ability to learn and understand input data (eg. understanding scenes in image analysis). Such learning models and algorithms are to a greater or lesser extent inspired by biological neural networks and their functioning, but in the end they deviate significantly from them. Nevertheless, any successful deep learning model represents a potential explanation of the unknown functioning of biological neural networks, their learning and their understanding of input information.

Enrolment requirements and/or entry competences required for the course

-

Learning outcomes at the level of the programme to which the course contributes

• Apply theoretical knowledge of the fundamentals of the six core disciplines and their relationship within cognitive science.
• Critically evaluate cognitive science findings and synthesize information to be employed in a collaborative professional environment.
• Participate in data-driven innovation projects and apply appropriate data science tools.
• Apply AI tools in concrete tasks and practical contexts.

Course content (syllabus)

• Neural networks introduction
• Artificial neural network basic concepts and building blocks
• Artificial neural network basic architectures
• Artificial neural network basic learning algorithms
• Deep neural network strengths and weaknesses. Applications.
• Deep neural network specifics: activation functions, regularization, momentum, adaptive learning
• Deep neural network specifics: activation functions, regularization, momentum, adaptive learning
• Deep convolutional networks: layers, architectures, visualization, fine tuning, applications, implementation
• Deep convolutional networks: layers, architectures, visualization, fine tuning, applications, implementation
• Fully convolutional networks: architectures, applications
• Interpretability of neutral network results
• Deep recurrent neural networks: RNN, bidirectional RNN, deep RNN, long short-term memory, sequence modelling, applications
• Deep recurrent neural networks: RNN, bidirectional RNN, deep RNN, long short-term memory, sequence modelling, applications
• Deep generative models: stacked RBMs, convolutional autoencoders, variational autoencoders, adversarial models
• Deep generative models: stacked RBMs, convolutional autoencoders, variational autoencoders, adversarial models

Student responsibilities

Class attendance. Engagement in classroom activities.

Required literature

• -

Optional literature

• Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press. 2017.
• Eli Stevens, Luca Antiga, Thomas Viehmann. Deep Learning with PyTorch. Manning Publications 2020.