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
Completion
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
- Signals and Systems (ISS)
- Computer Graphics Principles (IZG)
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
- 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
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
Course inclusion in study plans