Deep Learning E-DAT304

Deep Learning is a subset of Machine Learning which is completely based on artificial neural networks. As neural networks are designed to mimic the human brain, deep learning is likewise a human brain mimic. These neural networks are made up of a simple mathematical function that can be stacked on top of each other and arranged in the form of layers, giving them a sense of depth, hence the term Deep Learning.

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9.500 NOK + semester fee and literature

Learn more about the power of neural networks and get a better understanding of artificial with our Deep Learning course.

Course manager Mina Farmanbar


Kvinne som tenker

Deep Learning has a plethora of applications in almost every field such as health care, finance, and image recognition. It can be used to solve any pattern recognition problem and without human intervention. There are several popular and widely used deep learning frameworks that help to build neural network models. Some of the common ones are Tensorflow, Keras, PyTorch, and DL4J.

In this course, in addition to getting a solid understanding of deep learning, you will get hands-on experience by solving practical, real-life tasks using state-of-the-art techniques and software frameworks from machine learning, and deep learning. The concepts covered in this course provide relevant theoretical and hands-on programming knowledge. 

This course covers the following topics:

  • Introduction to Deep Learning (Deep Learning Fundamentals, AI vs. Machine Learning vs. Deep Learning - Relationship Overview)
  • Artificial Neural Networks (Perceptrons, Intro to Artificial Neural Networks, Layers in Artificial Neural Networks, Activation Functions in Artificial Neural Networks, Loss Functions in Artificial Neural Networks, Training Artificial Neural Networks, Batch Size & Epochs in Artificial Neural Networks, Optimization Algorithms in Artificial Neural Networks, Learning Rates in Artificial Neural Networks, Backpropagation Intuition - Neural Network Training, Bias in Artificial Neural Networks) .
  • Additional Fundamental topics (Datasets for Deep Learning - Training, Validation, & Test Sets, Overfitting- Artificial Neural Networks, Underfitting- Artificial Neural Networks, Tensor flow, and Keras, Multi-Layer Perceptron (MLP), Classification with the Tensor flow and MLP, Regression with the Tensor flow and MLP)
  • Convolutional Neural Networks (CNNs) (What are CNNs? Visualizing convolutional filters, Zero padding, Max pooling)
  • Recurrent Neural Networks (RNN) (What are RNNs? Architecture, Applications of RNNs)
  • LSTM (General structure of LSTM neural network, RNN neural network, and long-term dependence, LSTM neural network and long-term dependence, LSTM network architecture.

After taking this course, you will:

  • have a firm understanding of the fundamentals of modern neural networks and their practical use. 
  • Identify the deep learning algorithms which are more appropriate for various types of learning tasks in various domains. 
  • Implement deep learning algorithms and solve real-world problems. 

This is an online course. All the lectures are published as videos at once and students have immediate access to the entire course content. There will also be  three voluntary live sessions to provide an overview of the assignment and its solution, highlighting key points and discussing any challenges or areas of improvement.  

  • Course Starts: Monday, September 4 with an online kick-off meeting
  • Assignment #1
    • Start: September 25
      • Live discussion on October 2
    • End: October 13
  • Assignment #2
    • Start: October 16
      • Live discussion on October 23
    • End: November 3
  • Course End: November 10
  • Date: 13th of November 09:00 - 20th of November 13:00.

Recommended prerequisite knowledge:

DAT120 Introduction to Programming

General admission requirements:

Higher Education Entrance Qualification (GSK = grunnleggende studiekompetanse) or prior learning (realkompetanse).

Read more about the admission requirements here: Universitet og høgskole - Samordna opptak. If you apply on the basis of formal competence , the necessary documentation must be uploaded at the same time as you apply.

Admission based on prior learning (realkompetanse)

If you wish to apply for admission to higher education, are aged 25 or over and do not have higher education entrance qualifications, you may apply on the basis of prior learning. The University of Stavanger itself has the authority to assess what qualifications that are required. Please upload a CV and work-certificate.

Applicants with a foreign education 

You must document their higher education entrance qualification according to the GSU-list. You can find more information about the GSU-list here: by choosing the country where your education is taken. The language requirement is mandotary for English and Norwegian.

You must upload an offical translated diploma in either English or a Scandinavian language before submission.

Language requirements

Applicants with Norwegian or English as a second language must document sufficient knowledge of Norwegian or English.

To learn more about the language requirement go to Samordnaopptak

You can also go to NOKUT to see which countries require an English test (GSU list) GSU-listen | Nokut.

If you do not meet the language requirements above you may apply on the basis of prior learning. If the default language at work is english, please upload a document from your manager/HR manager that confirms your language proficiency.

E-book: Dive into Deep Learning, Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola,
Template for course plan

Book: Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and More with TensorFlow 2 and the Keras API,

The course will be set up as long as there is a sufficient amount of applicants


Associate Professor
Faculty of Science and Technology
Department of Electrical Engineering and Computer Science

Administrative contacts

Executive Officer
Division of Education
UiS Lifelong Learning
Higher Executive Officer
Division of Education
UiS Lifelong Learning
Senior Adviser
Division of Education
UiS Lifelong Learning