Course detail

Machine Learning and Recognition

FIT-SURAcad. year: 2023/2024

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

5

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

Basic literature

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

Recommended literature

Hart, P. E., Stork, D. G.:Pattern Classification (2nd ed), John Wiley & Sons, 2000, ISBN: 978-0-471-05669-0.

Classification of course in study plans

  • Programme MITAI Master's

    specialization NISY , 0 year of study, summer semester, elective
    specialization NSPE , 0 year of study, summer semester, compulsory
    specialization NBIO , 0 year of study, summer semester, elective
    specialization NSEN , 0 year of study, summer semester, elective
    specialization NVIZ , 0 year of study, summer semester, elective
    specialization NGRI , 0 year of study, summer semester, elective
    specialization NADE , 0 year of study, summer semester, elective
    specialization NISD , 0 year of study, summer semester, elective
    specialization NMAT , 0 year of study, summer semester, elective
    specialization NSEC , 0 year of study, summer semester, elective
    specialization NISY up to 2020/21 , 0 year of study, summer semester, compulsory
    specialization NCPS , 0 year of study, summer semester, elective
    specialization NHPC , 0 year of study, summer semester, elective
    specialization NNET , 0 year of study, summer semester, elective
    specialization NMAL , 0 year of study, summer semester, compulsory
    specialization NVER , 0 year of study, summer semester, elective
    specialization NIDE , 0 year of study, summer semester, elective
    specialization NEMB , 0 year of study, summer semester, elective
    specialization NEMB up to 2021/22 , 0 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Lukáš Burget, Ph.D.

Syllabus

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

Seminar

13 hod., compulsory

Teacher / Lecturer

doc. Ing. Lukáš Burget, 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