Course detail
Machine Learning Fundamentals
FEKT-MPA-MLFAcad. year: 2022/2023
The course deals with classical machine learning methods, such as support vector machines or principal component analysis, as well as with the approaches based on artificial neural networks, including convolutional or recursive networks. In addition to lectures, an important part of the course are exercises focused both on understanding the basic principles and on the use of machine learning in the field of radio communications, ranging from the classification of radio transmitters to a complete transmission system based on machine learning.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Offered to foreign students
Learning outcomes of the course unit
Prerequisites
A student who enrolls in a course should:
- understand basic mathematical methods at the bachelor's degree level
- be able to write a simple program in the Matlab environment and one of the higher programming languages
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Project: 50 points
Exam (written test): 24 points
Exam (oral interview, optional): 10 points
Course curriculum
1 – Course organization, introduction to machine learning, motivation
2 – Basics of linear algebra needed for machine learning
3 - Support vector Machines
4 - Principal component analysis
5 – Introduction to neural networks, representation, classification
6 – Training of neural networks (linear regression, gradient methods, polynomial regression …)
7 - Convolutional neural networks
8 - Recursive neural networks
9 – Tuning of hyperparameters, batch normalization, frameworks
10 – Unsupervised learning
11 – Generative networks, autoencoders, GAN
12 – Machine learning in large scale
13 – Use of machine learning in radio communications
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Mueller, J.P., Massaron, L. Machine Learning For Dummies; 1st edition, 2016, ISBN : 1119245516 (EN)
Smola, A., Vishwanathan, S.V.N., Introduction to Machine Learning, Cambridge University press, available at https://alex.smola.org/drafts/thebook.pdf (EN)
Recommended reading
Elearning
Classification of course in study plans
- Programme MPAJ-TEC Master's 1 year of study, summer semester, compulsory-optional
- Programme MPA-TEC Master's 1 year of study, summer semester, compulsory-optional
- Programme MPC-EKT Master's 1 year of study, summer semester, compulsory-optional
- Programme MPZ-EKT Master's 1 year of study, summer semester, compulsory-optional
Type of course unit
Elearning