Title:

Classification and Recognition

Code:IKR
Ac.Year:2017/2018
Term:Summer
Curriculums:
ProgrammeBranchYearDuty
IT-BC-3BIT-Elective
IT-BC-3BIT2ndElective
Language:Czech
Public info:http://www.fit.vutbr.cz/study/courses/IKR/public/
Credits:5
Completion:examination (written&verbal)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:26130013
 ExaminationTestsExercisesLaboratoriesOther
Points:60150025
Guarantee:Burget Lukáš, doc. Ing., Ph.D., 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.St.G.EndG.
MonlecturelecturesG20217:0019:502BIAxxxx
MonlecturelecturesG20217:0019:502BIBxxxx
MonlecturelecturesG20217:0019:503BITxxxx
 
Learning objectives:
  To understand the foundations of classification and recognition and to learn how to apply basic algorithms and methods in this field to problems in speech recognition, computer graphics and natural language processing. To get acquainted with the evaluation procedures. To conceive basics of statistical pattern recognition, discriminative training and building hybrid systems.
Description:
  The tasks of classification and pattern recognition, basic schema of a classifier, data and evaluation of individual methods, statistical pattern recognition, Bayes learning, maximum likelihood method, GMM, EM algorithm, discriminative training, kernel methods, hybrid systems, how to merge classifiers, basics of AdaBoost, structural recognition, speech processing applications - speaker recognition, language identification, speech recognition, keyword spotting, image processing - 2D object recognition, face detection, OCR, and natural language processing - document classification, text analysis.
Knowledge and skills required for the course:
  Basic knowledge of the standard math notation.
Subject specific learning outcomes and competences:
  The students will get acquainted with classification and recognition techniques and learn how to apply basic methods in the fields of speech recognition, computer graphics and natural language processing. 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 competences:
  The students will learn to work in a team. They will also improve their programming skills and their knowledge of development tools.
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
Fundamental literature:
 
  • 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.
Study literature:
 
  • Bishop, C. M.: Pattern Recognition, Springer Science + Business Media, LLC, 2006, ISBN 0-387-31073-8.
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
Exam prerequisites:
  
  • Realized project