Course details

Machine Learning and Recognition

SUR Acad. year 2024/2025 Summer semester 5 credits

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.

Guarantor

Course coordinator

Language of instruction

Czech

Completion

Examination (written+oral)

Time span

  • 26 hrs lectures
  • 13 hrs seminar
  • 13 hrs projects

Assessment points

  • 60 pts final exam (written part)
  • 15 pts mid-term test (written part)
  • 25 pts projects

Department

Lecturer

Instructor

Learning objectives

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.

Recommended prerequisites

Prerequisite knowledge and skills

Basic knowledge of the standard math notation.

Study literature

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

Fundamental literature

Syllabus of lectures

  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

Syllabus of seminars

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.

Syllabus - others, projects and individual work of students

  • Individually assigned projects

Progress assessment

  • 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

Schedule

DayTypeWeeksRoomStartEndCapacityLect.grpGroupsInfo
Wed lecture lectures E105 16:0017:5070 1MIT 2MIT NMAL NSPE xx Burget
Wed seminar lectures E105 18:0018:5070 1MIT 2MIT NMAL NSPE xx Burget

Course inclusion in study plans

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