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Deep Learning, 7.5 Credits

Djupinlärning, 7,5 Högskolepoäng

Established: 2022-09-01

Established by: Department of Engineering Science

Applies from: V23

Learning outcomes

After completing the course, the student should be able to:

Knowledge and understanding

• show understanding of the way artificial neural networks (ANN) and Deep Neural Network (DNN) get trained.
• explain the signal flow in an ANN.
• discuss the workings of some of the modern ANN and DNN architectures, e.g. CNN, LSTM, GAN etc.
• demonstrate knowledge and understanding of the "no free lunch theorem" in the sense of appreciating the limitations and strengths of various ANN/DNN architectures.

Competence and skills

• select the right approach to solve a practical problem using ANN/DNN.
• utilise existing libraries to build ANN/DNN applications.
• design a pertinent set of performance matrices based on which they can validate the performance of their algorithm(s).
• evaluate the performance of algorithms based on a pragmatic set of performance matrices.
• use the results from the experiments to re-design and finetune the ANN/DNN architectures so as to meet the requirements of the task.

Judgement and approach

• relate to some of the salient aspects of the EU AI Regulation.

Entry requirements

General entry requirements and approved result from the following course/courses: IAI600-Introduction to Artificial Intelligence and Machine Learning and BSD600-Big Data Processing and Analysis and PFA600-Programming for Automation and STB600-Sensor Technology and Image Analysis or the equivalent.

The forms of assessment of student performance

Written laboratory-work report in group. Written project report in group with oral presentation.

Course contents

applications. The following are the main contents of this course:

• Revision of linear algebra and calculus necessary to understand ANN and DNN.
• Revision of generic AI principles (e.g. bias-variance trade-off, meaning of validation vs test etc.).
• Perceptron and back-propagation algorithm.
• Deep learning networks.
• Some interesting DNN networks (e.g. CNN, LSTM etc.)
• Applications in some domains (e.g. computer vision, NLP etc.).
• Some modern DNN architectures (e.g. encoder-decoder, GAN etc.).
• Practical aspects of applying DNN/ANN in a real-life challenge.

Other regulations

Course grading: F/Fx/E/D/C/B/A - Insufficient, Insufficient- more work required before the credit can be awarded, Sufficient, Satisfactory, Good, Very Good, Excellent

Course language: The teaching is conducted in English.

General rules pertaining to examination at University West are available at www.hv.se.

If the student has a decision/recommendation on special support due to disability, the examiner has the right to examine the student in a customized examination form.

Cycle

Second cycle

Progressive specialization

A1F - Second cycle, has second-cycle course/s as entry requirements

Main field of study

Automation, Computer Engineering, Computer Science