Deep machine learning
The purpose with this course is to give a thorough introduction to deep machine learning, also known as deep learning or deep neural networks.
Over the last few years, deep machine learning has dramatically changed the state of the art performance in various fields including speech-recognition, computer vision and reinforcement learning (used, e.g., to learn how to play Go). We focus primarily on basic principles regarding how these networks are constructed and trained, but we also cover many of the key techniques used in different applications. The overall objective is to provide a solid understanding of how and why deep machine learning is useful, as well as the skills to apply them to solve problems of practical importance.
In the course, the following broad areas will be covered:
- supervised learning by cross-entropy minimisation and cross-validation back propagation and stochastic gradient descent
- a suitable programming language for implementing deep learning algorithm
- feedforward neural networks and convolutional neural networks
- recurrent neural networks and long short-term memory networks
- techniques for efficient training such as momentum and batch normalisation
- modern variations of neural networks (e.g., attention and residual networks)
- transfer learning and data augmentation
- reinforcement learning, Markov decision problems, q-learning and deep q-learning
- application of convolutional neural networks on image recognition and reinforcement learning