Prof. Dr. Paolo Favaro
Mr. Givi Meishvili
Mr. Simon Jenni
Mr. Adam Bielski
|Location||Seminarraum 002 HRZ building Engehaldenstrasse 8|
|Time||Tuesdays 09.15-11.00 (lecture) and 11.15-12.00 (tutorials)|
|Exam||June 11 2019 from 10:00-12:00 at Engehaldenstrasse 8, Room 002|
This course provides an introduction to deep learning methods. These are modern methods in artificial intelligence (AI), which are incorporated into most of the top-performing algorithms in several fields of research. We focus on the machine learning paradigm, where rules are learned from examples, rather than being hard-coded. Most examples will be in computer vision, that is, about problems of object recognition, detection, segmentation in images and videos. On a successful completion of this course, you may be able to get to know how to use the software to automate routine work, understand images and videos (and you should be able to work with other data too), and support basic scientific research.
The course will cover most of the chapters of the deep learning book by Goodfellow, Bengio, and Courville: Review of Machine Learning, Deep feedforward networks (gradient-based learning, back-propagation, regularization, optimization, training) Convolutional Neural Networks, Recurrent Neural Networks, Autoenders, Representation learning.
Applied math fundamentals like linear algebra, probability and numerical optimization.
Deep learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (available online at www.deeplearningbook.org)
Neural Networks and Deep Learning by Michael Nielsen (free online book at www.neuralnetworksanddeeplearning.com)
Pattern Recognition and Machine Learning by Christopher M. Bishop
On successful completion of this course students are expected to:
There will be a project and 5 small biweekly assignments. The project will be carried out with group work while the assignments will be carried out individually. The project will require 4 short group presentations and the submission of an individual report and code. All the work will contribute to the final grade as follows:
The project report should be prepared as a jupyter notebook, similar to this sample report.
The following table provides an overview of the content of the lectures during the semester. Please check it periodically as it might be updated.
|2||Machine Learning Review||Ch 5, DL book|
|3||Deep feedforward nets||Ch 6, DL book|
|4||Deep feedforward nets||Ch 6, DL book|
|5||Deep feedforward nets – regularization||Ch 7, DL book|
|6||Deep feedforward nets - optimization||Ch 8, DL book|
|7||Deep feedforward nets – training||Ch 8, DL book|
|8||ConvNets||Ch 9, DL book|
|9||Deep RNN||Ch 10, DL book|
|10||Deep RNN||Ch 10, DL book|
|11||Practical methodology||Ch 11, DL book|
|12||Autoencoders||Ch 14, DL book|
|13||Representation learning||Ch 15, DL book|