Welcome to Deep Learning


Description: Deep learning is a newly emerged area of research in machine learning and has recently shown huge success in a variety of areas. The impact on many applications is revolutionary, which ignites intensive studies of this topic.

During the past few decades, the prevalent machine learning methods, including support vector machines, conditional random fields, hidden Markov models, and one-hidden-layer multi-layer perceptron, have found a broad range of applications. While being effective in solving simple or well-constrained problems, these methods have one drawback in common, namely they all have shallow architectures. They in general have no more than one or two layers of nonlinear feature transformations, which limits their performance on many real world applications.

On the contrary, the human brain and its cognitive process, being far more complicated, have deep architectures that are organized into many hierarchical layers. The information gets more abstract while going up along the hierarchy. Interests in using deep architectures were reignited in 2006 when a deep belief network was shown to be trained well. Since then deep learning methods and applications have witnessed unprecedented success.

Learning objectives:

This course will give an introduction to deep learning both by presenting valuable methods and by addressing specific applications. This course covers both theory and practices for deep learning. Topics will include

  • Machine learning fundamentals
  • Deep learning concepts
  • Deep learning methods including deep autoencoders, deep neural networks, recurrent neural networks, and long short-term memory recurrent networks.
  • Selected applications of deep learning
  • Software and tools

Prerequisites: Basic probability and statistics theory, linear algebra and machine learning.

Teaching methods: Lectures and exercises.

Criteria for assessment: Attending the lectures.

Key literature: Li Deng and Dong Yu, Deep Learning: Methods and Applications, Now publishing, 2014.


Organizer and lecturer: Associate Professor Zheng-Hua Tan, email: zt@es.aau.dk


Time: 10 May 2017: 13:00 – 16:00 and 11 May 2017: 9:00 – 12:00 + 13:00 – 16:00.

Place: Aalborg University

Zip code: 9220

City: Aalborg Ø

Number of seats: 35

Deadline: 19. April 2017

Important information concerning PhD courses We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.