Deep Learning (2020)

Description:

Description: Deep learning is an 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 flelds, 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 have been reignited by many amazing applications enabled by deep learning techniques.

During the past years, deep learning methods and applications have witnessed unprecedented success.

This course will give an introduction to deep learning both by presenting valuable methods and by addressing speciflc applications. This course covers both theory and practices for deep learning. The students will also have hands-on exercises experimenting a variety of deep learning architectures for applications.

Topics will include:

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

Teaching methods: The course will be taught through a combination of lectures, demos of applications and small exercises.

Criteria for assessment: Acceptable exercise solutions and at-least 75% participation are required to pass the course


Organizer: Associate Professor Zheng-Hua Tan - zt@es.aau.dk

Lecturers: 

ECTS: 2.0

Time: 20, 22, 25 April 2020

Place: Aalborg University

Zip code: 9220

City: Aalborg

Number of seats: 50

Deadline: 29 March 2020


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 3,000 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 four 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.