Course detail

Classification and Recognition

FIT-IKRAcad. year: 2018/2019

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 to speech and image processing.

Learning outcomes of the course unit

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.

Prerequisites

Basic knowledge of the standard math notation.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

  • Mařík, V., Štěpánková, O., Lažanský, J. a kol.: Umělá inteligence (1-4), ACADEMIA Praha, 1998-2003, ISBN 80-200-1044-0.

  • Bishop, C. M.: Pattern Recognition, Springer Science + Business Media, LLC, 2006, ISBN 0-387-31073-8.
  • Fukunaga, K. Statistical pattern recognition, Morgan Kaufmann, 1990, ISBN 0-122-69851-7.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes


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

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

    Syllabus of lectures:
    1. The tasks of classification and pattern recognition, basic schema of a classifier, data sets and evaluation
    2. Probabilistic distributions and linear models
    3. Statistical pattern recognition, Bayes learning, maximum likelihood method
    4. Sequential data modeling, hidden Markov models, linear dynamical systems
    5. Generative and discriminative models
    6. Speech processing applications - speaker recognition, language identification, speech recognition, keyword spotting
    7. Kernel methods
    8. Mixture models, EM algorithm
    9. Combining models, boosting
    10. AdaBoost, basics and extensions of the model
    11. Image processing - 2D object recognition, face detection, OCR
    12. Pattern recognition in text, grammars, languages, text analysis
    13. Project presentation, future directions

    Syllabus - others, projects and individual work of students:
    • Individually assigned projects

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.

Specification of controlled education, way of implementation and compensation for absences

The evaluation includes 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

Classification of course in study plans

  • Programme IT-BC-3 Bachelor's

    branch BIT , 2. year of study, summer semester, 5 credits, optional

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus


  1. The tasks of classification and pattern recognition, 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

Exercise

13 hours, compulsory

Teacher / Lecturer

Projects

13 hours, compulsory

Teacher / Lecturer

Syllabus


  • Individually assigned projects

eLearning

eLearning: opened course