Deep Learning

Course description

This course provides an introduction to Deep Learning methods. These are modern methods in artificial intelligence (AI), which are today incorporated in 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 can expect to be able to develop intelligent software to automate routine labor, understand images and videos (but you should be able to work with other data too), and support basic scientific research.

The course will cover most of the contents of the Deep Learning book by Goodfellow et al.: review of Machine Learning, Deep feedforward networks (anatomy of a neural network, gradient-based learning, back-propagation, regularization, optimization, training), Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, Representation learning.

Prerequisites

Applied math fundamentals like linear algebra, probability and numerical optimization.

Resources

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

Learning Outcomes

On successful completion of this course students are expected to:

1. be able to formalize tasks in computer vision via neural networks
2. design neural networks to solve data-rich tasks
3. build datasets, tune and train neural networks with advanced deep learning libraries
4. understand the inner mechanisms of neural networks during training
5. analyze the performance of neural networks on tasks of interest.