Pattern Recognition

Pattern Recognition (Elective), Jan - Apr, 2020

Instructor Details

Instructor 1

Name: Ajaya Kumar Dash
Email:
Address:
- CB-51, Faculty chambers, IIIT Bhubaneswar

Instructor 2

Name: Dr Swati Vipsita
Email:
Address:
- CB-50, Faculty chambers, IIIT Bhubaneswar

Online Class schedule ( ≈ 3 hrs per Week)

Day → Details ↓	Friday	Saturday	Sunday
Week 1	03/04/2020 9:00 am	04/04/2020 9:00 am	-
Week 2	-	11/04/2020 9:00 am	-
Week 3	-	18/04/2020 9:00 am	-
Week 4	-	-	26/04/2020 10:00 am
Week 5	-	02/05/2020 9:00 am	03/05/2020 10:00 am
Week 6	-	-	10/05/2020 10:00 am

Note: Download zoom.us and register with your institute email.

Objective

Get acquainted with the design and construction of a pattern recognition system
Exposure to major approaches available in pattern recognition
To get the idea of theoretical issues associated while designing a pattern recognition systems
Hands on experience by implementing some popular pattern recognition techniques

Learning Outcomes

Explain and compare a variety of pattern classification, structural pattern recognition, and pattern classifier combination techniques
Apply pattern recognition techniques to real-world problems such as image recognition, image classification, document analysis etc
Implement simple pattern classifiers, classifier combinations, and structural pattern recognizers etc

Syllabus

Find the syllabus by visiting the link. [ Download ]

Books

Text Book

T1. Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, John Wiley & Sons, 2001.

Reference Books

R1. Theodoridis, Pattern Recognition, 4th Edition, Elsevier, 2014.
R2. Christopher M. Bishop, Pattern Recognition and Machine Learning, 1st Edition, Springer, 2006. (Corr. 2nd printing 2011 edition.
R3. V Susheela Devi and M Narasimha Murty, Pattern Recognition: An Introduction, 1st Edition, Universities Press, 2011.

Evaluation

Course Progress and Resources

Some fundamental Research Articles
- Efficient Feature Selection via Analysis of Relevance and Redundancy, suggested by: Dr. S Vipsita, [ Link ]
- A fuzzy K-nearest neighbor algorithm, suggested by: Dr. S Vipsita, [ Link ]
- Feature selection for classification, suggested by: Dr. S Vipsita, [ Link ]
- Statistical Pattern Recognition: A Review, suggested by: A K Dash, [ Link ]
- Tutorial on Maximum likelihood estimation, suggested by: A K Dash, [ Link ]
Probable Course Progess (By A K Dash)

Attention: Due to CoronaVirus outbreak, i will take the classes online.

Class No	Date	Time	Duration	ReadingList / Resources	Contents Discussed
1	-	-	1.0 hr	Chapter-1 of T1 and R1	Introduction to PR and Applications
2	-	-	1.0 hr	Chapter-1 of T1; Section 2.1 of T1	Overview of PR system, Supervised unsupervised and semi-supervised classification, Introduction to Bayesian decision theory
3	-	-	1.0 hr	Section 2.1 of T1	Bayesian Classifier for 2 class problem and Average Error
4	-	-	1.0 hr	Section 2.2 and 2.3 of T1 [ Lect1 ] [ Lect2 ]	Bayesian Decision Theory-Continuous Features, Minimum error rate classification
5	-	-	1.0 hr	Section 2.4, Section: 2.5.1 and 2.5.2 of T1 (refer the slides of Class 8)	Discriminant Function, Minimum error rate classification through discriminant function, Normal density (Univariate and Multivariate expression)
6	-	-	1.0 hr	Class Notes	Concept of Covariance matrix, physical interpretation, 1D Normal Density, Multi-dimensional Normal density, Physical Interpretation
7	-	-	1.0 hr	Section 2.6 (2.6.1) of T1 (refer the slides of Class 8)	Discriminant Function for the Normal Density (Special Case - I)
8	11/04/20	09:00	1.0 hr	Section 2.6 (2.6.2, 2.6.3) of T1 [ Link ]	Discriminant Function for the Normal Density (Special Case - II and Case III)
9	18/04/20	09:00	1.0 hr	Class Note [ Link ]	Examples of discriminant function for the Normal Density (Special Case - II and Case III)
10	26/04/20	10:00	1.0 hr	Section 2.7 and 2.8 of T1 [ Link ]	Bayes decision error and error bounds
11	02/05/20	09:00	1.0 hr	Section 2.9 of T1 [ Link ]	Bayes decision theory for discrete features, Understanding maximum likelihood
12	03/05/20	09:00	1.0 hr	Section 3.1 and 3.2 of T1 [ Link ]	Maximum likelihood parameter estimation, example
13	10/05/20	10:00	1.0 hr	Section 3.7 and 3.8 of T1 [ Link1 ] [ Link2 ]	Curse of dimensionality, dimensionality reduction, PCA (theory and example)

Instructor Details

Instructor 1

Instructor 2

Online Class schedule ( ≈ 3 hrs per Week)

Objective

Learning Outcomes

Syllabus

Books

Text Book

Reference Books

Evaluation

Course Progress and Resources

Some fundamental Research Articles

Probable Course Progess (By A K Dash)