Subject: Machine learning in embedded systems (17 - EM506)


Basic Information

CategoryProfessional-applicative
Scientific or art field:Electronics
InterdisciplinaryNo
ECTS4
Native organizations units

Course native organizational units not found!
Course specification

Course is active from 31.10.2018..


Precondition courses

Course idMandatoryMandatory
Discrete Signals and SystemsYesYes
Design of Complex Digital SystemsYesYes
The main goal of this course is to introduce students to the basic and some advanced approaches, trends and tools in the field of machine learning systems. Students will also be able to develop machine learning systems intended to be used within embedded electronic systems.
Students who successfully complete this course should be able to follow the latest results, as well as to understand the latest technical and scientific literature in this field. Beside theoretical knowledge, students will also gain experience in using contemporary design tools used to develop machine learning systems. Students will also be able to develop artificial intelligence systems, based on machine learning, that will be used in embedded systems.
Introduction to machine learning. Formal learning model. Model selection and validation. Regularization and stability. Linear predictors. Support vector machines. Kernel methods. Decision trees. Artificial neural networks. Online learning. Incremental learning. Adaptive learning. Clustering. Dimensionality reduction. Feature selection. Generative models. Reinforcement learning. Deep learning. Ensemble learning. Machine learning implementation techniques targeting embedded systems. Hardware accelerators for machine learning.
Lectures; Auditory Practice; Computer Practice; Laboratory Practice; Consultations.
AuthorsNameYearPublisherLanguage
Peter FlachMachine Learning - The Art and Science of Algorithms that Make Sense of Data2012Cambridge University PressEnglish
Course activity Pre-examination ObligationsNumber of points
Project defenceYesYes50.00
Theoretical part of the examNoYes50.00
Name and surnameForm of classes
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Struharik Rastislav
Full Professor

Lectures
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Vranjković Vuk
Associate Professor

Lectures
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Kisačanin Branislav
Assistant Professor

Lectures
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Batinić Branislav
Assistant Professor

Laboratory classes