Course
Deep Learning (E-DAT304)
The concepts covered in this course provide relevant theoretical and hands-on programming knowledge.
Course description for study year 2025-2026. Please note that changes may occur.
Facts
Course code
E-DAT304
Credits (ECTS)
5
Semester tution start
Autumn
Language of instruction
English
Number of semesters
1
Exam semester
Autumn
Time table
Literature
Content
- 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)
Learning outcome
After completing this course, you will:
- have a firm understanding of the fundamentals of modern neural networks and their practical use.
- Identify the deep learning algorithms that are more appropriate for various types of learning tasks in various domains.
- Implement deep learning algorithms and solve real-world problems
Required prerequisite knowledge
None
Recommended prerequisites
Introduction to Programming (DAT120)
Introduction to Programming
Exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Home exam | 1/1 | 7 Days | Letter grades |
Digital exam
- A project-based home exam that requires students to complete a project related to a practical task over the course of one week.
- The exam will be done on the UiS Inspera platform.
- Grading: The exam will be graded (A-F)
- Duration: one week
Coursework requirements
- There will be one individual use-case-based assignment in which students will be required to complete a practical task that involves applying the knowledge and skills they have learned in the course to a real-world scenario.
- The assignment is mandatory - approved / not approved.
- Submissions should provide coding solutions to the respective problem with proper documentation.
Course teacher(s)
Head of Department:
Tom RyenCourse coordinator:
Mina FarmanbarMethod of work
All the recorded lectures are published as videos at once and students have immediate access to the entire course content.
Open for
Course assessment
There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.
The course description is retrieved from FS (Felles studentsystem). Version 1