IKT450 Deep Neural Networks

The course is connected to the following study programs

• Artificial Intelligence and The Internet of Things, Master's Programme
• Artificial Intelligence, 5-year master programme

Teaching language

English

Course contents

The course covers theoretical and practical aspects of neural networks:

• fundamentals of neural networks including feed-forward models, optimizers, and training algorithms (e.g., backpropagation),
• neural network architectures including deep learning models, convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks, and
• working end-to-end pipelines including performance evaluation, detecting overfitting, underfitting, and data defects.

Learning outcomes

In this course, the students will learn the foundations of neural networks and deep learning. They will, at course completion, be able to build and train feed-forward and recurrent neural models, understand the critical parameters in neural architectures, and understand when to apply different models. The students will also learn the theoretical foundation of the models, types of layers, and optimizers, and learn how to design, implement, and analyze advanced shallow and deep neural networks. They will gain practical programming experience with deep neural networks highly relevant to the industry.

Teaching methods

Combination of lectures, assignments, paper studies, lab, and report writing. The tasks are done individually or in small groups with group supervision.

The work load for the average student is approximately 200 hours.

Evaluation

The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as Single Standing Module

Yes, subject to availability.

Admission Requirement if given as Single Standing Module

Admission requirements for the course are the same as for the master’s programme in ICT.

Assessment methods and criteria

Graded portfolio assessment. Information about the content of the portfolio will be given in LMS by the start of the semester.