IKT450 Deep Neural Networks
- ECTS Credits:
- 7.5
- Responsible department:
- Faculty of Engineering and Science
- Course Leader:
- Morten Goodwin
- Lecture Semester:
- Autumn
- Teaching language:
- English
- Duration:
- 1 term
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
EnglishCourse 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
On successful completion of the course, the student should:
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Have knowledge of the foundations of neural networks and deep learning.
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Be able to build and train feed-forward and recurrent neural models,
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Understand the critical parameters in neural architectures, and understand when to apply different models.
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Have knowledge of the theoretical foundation of the models, types of layers, and optimizers.
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be able to design, implement, and analyze advanced shallow and deep neural networks.
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have 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.There will be a postponed exam in portfolio assessment.