Learning from Unstructured Data
Objectives
At the end of this unit, students will:
Understand:
-basic principles of deep learning.
automated extraction of features and representations for use in regression or classification in multi-layer models.
optimization and regularization methods applicable to models with a large number of parameters
- the different models presented: fully connected feed forward neural networks, convolution networks and recurrent networks.
- Problems and techniques for processing unstructured data.
Be able to:
-Select and implement models to solve some typical problems and optimize training to obtain a reasonable solution
Know:
Problems to which deep learning applies: object recognition in images, voice recognition, natural language processing, recommendation systems and others.
General characterization
Code
12084
Credits
6.0
Responsible teacher
Ludwig Krippahl, Pedro Manuel Corrêa Calvente Barahona
Hours
Weekly - 4
Total - 48
Teaching language
Português
Prerequisites
-
Bibliography
-Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press
-Yann LeCun , Yoshua Bengio and Geoffrey Hinton, Deep learning, Nature, Vol. 521, pp 436-444, 2015
Teaching method
Lectures will cover the fundamental topics of the subject matter, which the students should complement with the given bibliography. All lecture materials will be supplied for further study. Lectures will include some time for questions and discussion of the subject matter.
Tutorial classes will be dedicated to exercises and guidance in the practical assignments, focusing on selected topics.
Class schedules and materials will be supplied online, as well as additional information regarding the course.
Evaluation method
The fial grade is the weighted average of:
30%: theory test.
20%: First project.
50%: Final project.
Subject matter
1-Introduction: data types and problems.
2-Deep Feedforward networks
3-Optimization of Feedforward networks.
4-Regularization of Feedforward networks.
5-Convolution networks.
6-Recurrent networks and sequential problems.
7-Introduction to generator models and their application to unsupervised learning.
8- Semi-supervised learning, representation learning and transference learning
9- Additional problems and examples.