Code |
11512
|
Year |
1
|
Semester |
S1
|
ECTS Credits |
6
|
Workload |
OT(15H)
|
Scientific area |
Informatics
|
Entry requirements |
Know how to program and have notions of probability and statistics.
|
Mode of delivery |
Face-to-face
|
Work placements |
Not applicable.
|
Learning outcomes |
Introduce the concepts, models and language adequate to problem solving using neural networks. At the end of this course unit the student should be able to solve problems using neural networks and eventually be able to propose improvements to current methods.
|
Syllabus |
1-Feed-forwd and recurrent networks. 2-Networks for clustering, classification and regression. 3-Reservoir networks. 4-Learning algorithms. 5-Topologies. 6-Cost functions. 7-Performance evaluation. 8-Shallow versus deep networks. 9-Applications to signal and image processing.
|
Main Bibliography |
Main: -Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press book, 2016 -Neural Networks and Learning Machines (3rd Edition), S. Haykin, Prentice Hall; 3 edition, 2008
Complementary: -Pattern Recognition and Machine Learning, C. Bishop, Springer, 2006 -Several journals in this area.
|
Language |
Portuguese. Tutorial support is available in English.
|