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Course detail
FEKT-MKA-MLFAcad. year: 2023/2024
The course deals with both the classical machine learning methods such as support vector machines or principal component analysis and the machine learning techniques based on the neural networks, including convolutional and recursive networks. The concept of quantum computation and related algorithms is also introduced. Besides the lectures, the computer labs represent a very important part of the course, and serve as a tool to understand the basic principles of methods and their usage in communications.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Entry knowledge
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
Rules for evaluation and completion of the course
final project 40 points
final exam 34 points (24 compulsory written part, 10 optional oral part)
Aims
The aim of the course is to review necessary linear algebra methods, make students familiar with classical machine learning as well as with deep learning and its most important individual methods, and to introduce the basic concepts of quantum computins as one of the possible future directions. The computer lab excercises help students to get practical experience with related algorithms and software tools.
The graduate of the course will be able to (a) use basic machine learning methods for classification (b) use methods based on artificial neural networks (c) correctly choose a suitable machine learning method for the given task (d) discuss the use of machine learning methods in radio communications (e) discuss basic techniques of quantum computing and their use for given application
Study aids
Prerequisites and corequisites
Basic literature
Recommended literature
Elearning
Classification of course in study plans
Lecture
Teacher / Lecturer
Syllabus
1-Introduction to ML, course organization2-Basics of linear algebra, Principal Component Analysis, PCA3-Support Vector Machines (SVM)4-K-means, KNN classification5-Introduction to neural network (NN), linear/log regression6-NN problems and tuning7-Convolutional NN8-Transfer learning9-Recurrent NN, LSTM networks10-Generative networks, GAN networks11-Industrial talk – use of neural networks in manufacturing12-Quantum information - basic arithmetic13-Quantum information – quantum gates and circuits
Exercise in computer lab
1-Introduction, algebra, Collab2-PCA3-SVM4-K-means5-Neural Network (NN) introduction6-NN tuning, Miniproject assignment7-Miniproject8-Convolutional NN9-Recurent NN, LSTM10-project11-project12-Quantum arithmetic13-Quantum circuits