Title:

Classification and Recognition

Code:IKR
Ac.Year:2018/2019
Sem:Summer
Curriculums:
ProgrammeFieldYearDuty
IT-BC-3BIT-Elective
IT-BC-3BIT2ndElective
Language of Instruction:Czech
Public info:http://www.fit.vutbr.cz/study/courses/IKR/public/
Credits:5
Completion:examination (written&verbal)
Type of
instruction:
Hour/semLecturesSeminar
Exercises
Laboratory
Exercises
Computer
Exercises
Other
Hours:26130013
 ExamsTestsExercisesLaboratoriesOther
Points:60150025
Guarantor:Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Deputy guarantor:Černocký Jan, doc. Dr. Ing. (DCGM)
Lecturer:Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Černocký Jan, doc. Dr. Ing. (DCGM)
Španěl Michal, Ing., Ph.D. (DCGM)
Instructor:Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Graphics and Multimedia FIT BUT
Prerequisites: 
Computer Graphics Principles (IZG), DCGM
Signals and Systems (ISS), DCGM
Schedule:
DayLessonWeekRoomStartEndLect.Gr.Groups
WedlecturelecturesG202 17:0019:502BIA 2BIB 3BIT xx
 
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.
Description:
  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.

Knowledge and skills required for the course:
  Basic knowledge of the standard math notation.
Subject specific learning outcomes and competencies:
  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.
Generic learning outcomes and competencies:
  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.

Why is the course taught:
  Recent years witness a boom of machine learning or pattern recognition applications. More and more devices can be controlled using voice or gestures. Digital cameras automatically detect faces in the captured images in order to automatically focus or somehow react on it. Virtual agents in mobile devices can recognize speech and search for relevant answers to our queries. The quality of the current systems for automatic recognition of person's identity from voice recording or from face photo already significantly exceed the human abilities.

In this class, the students should learn how these technologies works. They will learn about the basic algorithms and models, which, using some training examples, automatically learn to recognize nontrivial patterns in audio recordings, images or other signals or input data.

Syllabus of lectures:
 
  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
Syllabus - others, projects and individual work of students:
 
  • Individually assigned projects
Fundamental literature:
 
  • Bishop, C. M.: Pattern Recognition, Springer Science + Business Media, LLC, 2006, ISBN 0-387-31073-8.
  • Hart, P. E., Stork, D. G.:Pattern Classification (2nd ed), John Wiley &
    Sons, 2000, ISBN: 978-0-471-05669-0
  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. The MIT Press.
Study literature:
 
  • Bishop, C. M.: Pattern Recognition, Springer Science + Business Media, LLC, 2006, ISBN 0-387-31073-8.
  • Hart, P. E., Stork, D. G.:Pattern Classification (2nd ed), John Wiley &Sons, 2000, ISBN: 978-0-471-05669-0
Controlled instruction:
  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
Progress assessment:
  
  • Mid-term test - up to 15 points
  • Project - up to 25 points
  • Written final exam - up to 60 points
 

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