Pattern Recognition
General data
Course ID: | 103C-CSCSN-MSA-EPART |
Erasmus code / ISCED: | (unknown) / (unknown) |
Course title: | Pattern Recognition |
Name in Polish: | Pattern Recognition |
Organizational unit: | The Faculty of Electronics and Information Technology |
Course groups: |
( Advanced Courses )--M.Sc.-EITI ( Advanced Courses )-Computer Information System Engineering-M.Sc.-EITI ( Computer Systems and Networks - Advanced )-Computer Systems and Networks-M.Sc.-EITI ( Courses in English )--eng.-EITI ( Technical Courses )---EITI ( Technical Courses )--eng.-EITI |
ECTS credit allocation (and other scores): |
6.00
|
Language: | English |
(in Polish) Jednostka decyzyjna: | (in Polish) 103000 - Wydział Elektroniki i Technik Informacyjnych |
(in Polish) Kod wydziałowy: | (in Polish) EPART |
(in Polish) Numer wersji: | (in Polish) 3 |
Short description: |
The aim of the lecture is to familiarize students with the issue of image recognition. In particular, the following topics will be discussed: general classification of image recognition systems, selected methods and techniques of image recognition as well as issues related to data collection, detection of outliers, dimensionality reduction and assessment of classification quality. The tasks carried out during laboratory exercises and mini-projects will allow the students to become familiar with the selected methods of image classification. |
Full description: |
The aim of the lecture is to familiarize students with the image recognition, starting from data collection and preprocessing, to selected methods of classification and assessment of the quality of classification. Lectures: Introduction: components of the image recognition system; design cycle of creating a classifier; methods of quality assessment of classifiers and classification systems. Optimal Bayesian Classification: the role of a priori information; the form of the probability density function; optimal Bayes classifier; the risk / loss of the classification decision; decision boundaries of classifiers; compliance of the data distribution with the adopted theoretical distribution. Nearest Neighbour Methods: Template Matching; minimum distance classifiers; metrics; k-NN classifiers; nearest neighbour search methods; speeding up the search for the nearest neighbour; editing and reduction of the training set. Linear Classification: linear decision functions; homogeneous space; learning the decision boundary; assembling of individual classifiers' results for more than two classes; support vector method; sequential minimal optimization algorithm. Dimensionality Reduction: Principal Component Analysis; Fisher's linear classification; multivariate discriminant analysis (MDA). Clustering: clustering problem; assessment of clustering similarity; k-means class algorithms; bottom-up clustering; graph algorithms. Neural Networks: basic model of the neuron; single neuron learning algorithms; interpretation of the operation of a single neuron; neural networks; error backpropagation algorithm; convolutional and feedback networks; application examples beyond image recognition. Markov Models: discrete Markov processes; hidden Markov process; Viterbi's algorithm; Baum-Welsh algorithm for determining the parameters of the Markov system; problems of using Markov models in classification. Text Searching: the problem of exact and approximate text search; Boyer-Moor algorithm; edit distance; text analysis with the use of nondeterministic and deterministic automata; tree and table of suffixes; Ukkonen's algorithm for constructing a suffix tree; approximate search with suffix trees; neighbourhood generation for search with errors; hash functions for quick searches. Decision Trees: constructing decision trees - basic CART algorithm; assessment of tree node heterogeneity; stop criteria when building a tree; horizon effect; tree pruning algorithms. Quality of Classification Improvement: basic problems of designing meta-classifiers; voting schemes; the issue of the independence of classifiers; voting with weights; determination of weights; Bayesian methods of composing the results of classifiers; Behaviour-Knowledge space; methods of constructing sets of weak classifiers (AdaBoost algorithm); use of contextual information in classification; context in OCR systems; use of dictionaries and trigrams. Laboratories Nearest Neighbour Classification (warm-up exercise - getting to know the Octave environment). Bayesian classification with normal distribution and approximation of the probability density distribution with the Parzen window. Quality of classification improvement - constructing a meta-classifier from the provided classifiers. Project Linear classification - reducing dimension with PCA; perceptron algorithm for determining the decision plane; OVO and OVR classifier assemblies; analysis of classification results - confusion matrix. Neural networks - implementation of the error back propagation algorithm; testing the influence of hyperparameters on the results of classification on the training and validation set. |
Bibliography: |
GNU Octave - Scientific Programming Language (https://www.gnu.org/software/octave/) |
Learning outcomes: |
Knowledge
Skills
|
Classes in period "Summer Semester 2023/2024" (in progress)
Time span: | 2024-02-19 - 2024-09-30 |
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MO PRO
LAB
TU WYK
W TH FR |
Type of class: |
laboratory, 15 hours, 24 places
lectures, 30 hours, 24 places
project , 15 hours, 24 places
|
|
Coordinators: | Rajmund Kożuszek | |
Group instructors: | Rajmund Kożuszek | |
Students list: | (inaccessible to you) | |
Examination: | Overall grade | |
(in Polish) Jednostka realizująca: | (in Polish) 103200 - Instytut Informatyki |
Classes in period "Winter Semester 2023/2024" (past)
Time span: | 2023-10-01 - 2024-02-18 |
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MO LAB
TU WYK
W TH LAB
LAB
FR |
Type of class: |
laboratory, 15 hours, 32 places
lectures, 30 hours, 32 places
project , 15 hours, 32 places
|
|
Coordinators: | Rajmund Kożuszek | |
Group instructors: | Rajmund Kożuszek | |
Students list: | (inaccessible to you) | |
Examination: | Overall grade | |
(in Polish) Jednostka realizująca: | (in Polish) 103200 - Instytut Informatyki |
Classes in period "Summer Semester 2022/2023" (past)
Time span: | 2023-02-20 - 2023-09-30 |
Navigate to timetable
MO PRO
LAB
TU WYK
W TH FR |
Type of class: |
laboratory, 15 hours, 32 places
lectures, 30 hours, 32 places
project , 15 hours, 32 places
|
|
Coordinators: | Rajmund Kożuszek | |
Group instructors: | Rajmund Kożuszek | |
Students list: | (inaccessible to you) | |
Examination: | Overall grade | |
(in Polish) Jednostka realizująca: | (in Polish) 103200 - Instytut Informatyki |
Classes in period "Winter Semester 2022/2023" (past)
Time span: | 2022-10-01 - 2023-02-19 |
Navigate to timetable
MO PRO
LAB
TU WYK
W TH LAB
PRO
PRO
LAB
FR |
Type of class: |
laboratory, 15 hours, 32 places
lectures, 30 hours, 32 places
project , 15 hours, 32 places
|
|
Coordinators: | Rajmund Kożuszek | |
Group instructors: | Rajmund Kożuszek, Henryk Rybiński, Muhammad Farhan Safdar | |
Students list: | (inaccessible to you) | |
Examination: | Overall grade | |
(in Polish) Jednostka realizująca: | (in Polish) 103200 - Instytut Informatyki |
Classes in period "Winter Semester 2021/2022" (past)
Time span: | 2021-10-01 - 2022-02-22 |
Navigate to timetable
MO TU WYK
W TH PRO
LAB
LAB
PRO
FR |
Type of class: |
laboratory, 15 hours, 32 places
lectures, 30 hours, 32 places
project , 15 hours, 32 places
|
|
Coordinators: | Rajmund Kożuszek | |
Group instructors: | Rajmund Kożuszek | |
Students list: | (inaccessible to you) | |
Examination: | Overall grade | |
(in Polish) Jednostka realizująca: | (in Polish) 103200 - Instytut Informatyki |
Classes in period "Winter Semester 2020/2021" (past)
Time span: | 2020-10-01 - 2021-02-19 |
Navigate to timetable
MO TU WYK
W TH LAB
LAB
FR |
Type of class: |
laboratory, 15 hours, 32 places
lectures, 30 hours, 32 places
project , 15 hours, 32 places
|
|
Coordinators: | Rajmund Kożuszek | |
Group instructors: | Rajmund Kożuszek | |
Students list: | (inaccessible to you) | |
Examination: | Overall grade | |
(in Polish) Jednostka realizująca: | (in Polish) 103200 - Instytut Informatyki |
Classes in period "Winter Semester 2019/2020" (past)
Time span: | 2019-10-01 - 2020-02-21 |
Navigate to timetable
MO LAB
TU WYK
W LAB
TH LAB
LAB
FR |
Type of class: |
laboratory, 15 hours, 32 places
lectures, 30 hours, 32 places
project , 15 hours, 32 places
|
|
Coordinators: | Rajmund Kożuszek | |
Group instructors: | Rajmund Kożuszek | |
Students list: | (inaccessible to you) | |
Examination: | Overall grade | |
(in Polish) Jednostka realizująca: | (in Polish) 103200 - Instytut Informatyki |
Classes in period "Winter Semester 2018/2019" (past)
Time span: | 2018-10-01 - 2019-02-17 |
Navigate to timetable
MO TU WYK
W TH LAB
FR |
Type of class: |
laboratory, 15 hours, 32 places
lectures, 30 hours, 32 places
project , 15 hours, 32 places
|
|
Coordinators: | Rajmund Kożuszek | |
Group instructors: | Rajmund Kożuszek | |
Students list: | (inaccessible to you) | |
Examination: | Overall grade | |
(in Polish) Jednostka realizująca: | (in Polish) 103200 - Instytut Informatyki |
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