IIM
MAAGGEE RREECCO
OGGNNIITTIIO
ONN
Course code: 6.9-WM-IB-S1-EiI-047_13
Obligatority: Optional
Language: Polish, English
Course director : dr. Andrzej Marciniak, Ph.D.
Form of
instruction
hours per
week
Semester
Number of
teaching
hours
Numberper
of
semester
teaching
Lecturer: dr. Andrzej Marciniak, Ph.D.
Form of receiving a credit
for a course
Number
of ECTS
credits
allocated
Full-time studies
Lecture
15
1
Laboratory
30
2
Project
15
1
Exam
VI
5
Grade
Grade
СOURSE AIMS:
Introduction to statistical pattern recognition. The students are provided with sufficient
knowledge for designing and evaluating classifiers using proper methods for the problem at
hand. Students will be able to implement a set of practical methods to solve real world
problems in medical diagnosis, biometrics and image processing.
PREREQUISITES:
Probability and statistics, linear algebra, math skills.
COURSE CONTENTS:
Introduction. Real world examples of pattern recognition applications. Formulation of basic
problems, notions and terms. Fundamentals of decision theory.
Reception and structure of feature space. Feature extraction and representation.
Dimensionality reduction, prototyping. Feature selection and searching strategies:
sequential and branch & bound approaches.
Estimation of prediction accuracy. Generalization and substitutions. Assessment and
validation methods: bootstrapping, cross-validation.
Linear discriminant analysis. Fisher’s discriminant, separability in feature space, regions and
decision boundaries, scatter plot.
Bayes decision theory and Bayes classifier. Decision rules, the Bayes decision rule for
minimum error, the Bayes decision rule for minimum cost, error probability in hypothesis
testing, upper bounds on the Bayes error. Bayes linear classifier, naïve approach, design of:
linear classifier, quadratic classifier, piecewise classifier.
Nonparametric classifiers. Nearest neighbor decision rules: editing, condensing and efficient
nearest neighbor search. KNN Density Estimation, Parzen Density Estimation.
Cluster analysis. Decision-directed learning, graph-theoretic methods, agglomerative and
divisive methods, ISODATA.
Syntactic and structural classification. String methods, Freeman chain coding, Shaw
description language.
Applications. Biometrics: face detection and recognition. Image processing: segmentation
and compression. Medical diagnosis: classification of breast cancer.
TEACHING METHODS:
Lectures with audiovisual aids. Group work in laboratory classes.
LEARNING OUTCOMES:
Learning outcomes
In the field of
technical sciences
Engineer
competency
Knowledge, skills, competences
T1A_W01,
T1P_W01
The student knows general
principles and methods of
decision
theory,
machine
learning and data clustering.
T1A_W07
The student is able to
characterize and implement
separate stages of pattern
recognition design cycle.
T1A_U15
InzA_U02
The student is able to select a
set of relevant features for
solving classification problem.
T1A_U08
InzA_U08
The student can design pattern
classifier and set up parameters.
T1A_U13
InzA_U05
The student is able to verify a
quality of classifier using
statistical techniques of model
validation.
T1A_U01
The student is able to interpret
research
results,
draw
conclusions and write short
scientific report.
LEARNING OUTCOMES VERIFICATION AND ASSESSMENT CRITERIA:
Lecture - exam based on written test. Laboratory – final grade comprises positive
evaluation of reports based on each laboratory class, attendance and initiative on the
part of the student. Project – evaluation of final report.
STUDENT WORKLOAD:
Working with teacher:
15 h lecture + 30 h laboratory + 15 h project
=
Preparing for classes:
Reading:
Preparing laboratory reports:
Preparing for grade:
Preparing project final report:
Total: 150 h
60 h
30 h
30 h
10 h
10 h
10 h
RECOMMENDED READING:
1. Bishop C.: Neural Networks for Pattern Recognition, Oxford University Press, 1995.
2. Duda P., Hart R. and Stork O.: Pattern Classification, 2nd edition, Wiley, 2000.
3. Fukunaga K.:, Statistical Pattern Recognition, 2nd edition, Morgan Kaufmann, 1990.
OPTIONAL READING:
1. Mitchell T.M.: Machine Learning, WCB/McGraw-Hill, 1997.
2. Vapnik V.N.: The Nature of Statistical Learning Theory, 2nd edition, Springer, 2000.