Course detail

Machine Learning and Recognition

FIT-SURAcad. year: 2024/2025

The tasks of classification and pattern recognition, basic schema of a classifier, data and evaluation of individual methods, statistical pattern recognition, feature extraction, multivariate Gaussian distribution,, maximum likelihood estimation, Gaussian Mixture Model (GMM), Expectation Maximization (EM) algorithm, linear classifiers, perceptron, Gaussian Linear Classifier, logistic regression, support vector machines (SVM), feed-forward neural networks, convolutional and recurrent neural networks, sequence classification, Hidden Markov Models (HMM). Applications of the methods of speech and image processing.

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

doc. Ing. Lukáš Burget, Ph.D.

Department

Department of Computer Graphics and Multimedia (UPGM)

Entry knowledge

Basic knowledge of the standard math notation.

Rules for evaluation and completion of the course

Mid-term test - up to 15 points
Project - up to 25 points
Written final exam - up to 60 points

To get points from the exam, you need to get min. 20 points, otherwise the exam is rated 0 points.

The evaluation includes a mid-term test, individual project, and the final exam. The mid-term test does not have a correction option, the final exam has two possible correction terms

Aims

To understand the foundations of machine learning with the focus on pattern classification and recognition. To learn how to apply basic algorithms and methods from this field to problems in speech and image recognition. To conceive basic principles of different generative an discriminative models for statistical pattern recognition. To get acquainted with the evaluation procedures.
The students will get acquainted with the problem of machine learning applied to pattern classification and recognition. They will learn how to apply basic methods in the fields of speech processing and computer graphics. They will understand the common aspects and differences of the particular methods and will be able to take advantage of the existing classifiers in real-situations.
The students will get acquainted with python libraries focused on math, linear algebra and machine learning. They will also improve their math skills (probability theory, statistics, linear algebra) a programming skills. The students will learn to work in a team.

Study aids

Not applicable.

Prerequisites and corequisites

recommended prerequisite
Signals and Systems
recommended prerequisite
Computer Graphics Principles

Basic literature

https://www.fit.vutbr.cz/study/courses/SUR/public/

Type of course unit

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Lukáš Burget, Ph.D.
Ing. Karel Beneš, Ph.D.

Syllabus

The tasks of classification and pattern recognition, the basic schema of a classifier, data sets and evaluation
Probabilistic distributions, statistical pattern recognition
Generative and discriminative models
Multivariate Gaussian distribution, Maximum Likelihood estimation,
Gaussian Mixture Model (GMM), Expectation Maximization (EM)
Feature extraction, Mel-frequency cepstral coefficients.
Application of the statistical models in speech and image processing.
Linear classifiers, perceptron
Gaussian Linear Classifier, Logistic regression
Support Vector Machines (SVM), kernel functions
Neural networks - feed-forward, convolutional and recurrent
Hidden Markov Models (HMM) and their application to speech recognition
Project presentation

Seminar

13 hod., compulsory

Teacher / Lecturer

doc. Ing. Lukáš Burget, Ph.D.
Ing. Karel Beneš, Ph.D.

Syllabus

Lectures will be immediately followed by demonstration exercises (1h weekly) where examples on data and real code will be presented. Code and data of all demonstrations will be made available to the students.

Project

13 hod., compulsory

Teacher / Lecturer

doc. Ing. Lukáš Burget, Ph.D.

Syllabus

Individually assigned projects

VUT

Faculties

University Institutes

Parts

Machine Learning and Recognition

Type of course unit