Here are rough topics of lectures. The pages, where the topic has 
been covered in the textbook are in parentheses. All pages are not 
covered in the lectures with same depth.

Textbook: Theodoridis, Koutroumbas: Pattern Recognition, 2nd ed., 
          Elsevier, Amsterdam, 2003

Lecture 1:
	- technical arrangements
	- introduction to pattern recognition (pp. 1-11)
	- Bayes rule for classification (pp. 13-21)

Lecture 2:
	- Bayesian classifier with multinormal distribution (pp. 22-25)
	- Minimum distance classifier (pp. 25-27)
	- idea of distribution estimation (pp. 27-44, just idea)
	- kNN classifier (pp. 44-46)

Lecture 3:
 	- Batch-mode perceptron (pp. 55-60)

Lecture 4:
	- "reward and punishment"-scheme perceptron (pp. 60-62) 
	- pocket algorithm (pp. 63)

Lecture 5:
 	- Mean Square Eroor estimation (pp. 65-68)
	- Sum of Error Squares estimation  (pp. 70-72)
	- introduction to Support Vector Machines (pp. 77-79)

Lecture 6: 
	- Support Vector Machines, separable classes (pp. 80-82)
	- Two-layer perceptron (pp. 92-100)
	- Three-layer perceptrons (pp. 101-102)

Lecture 7:
        - Backpropagation algorithm (pp. 104-110)

Lecture 8:
        - Bacpropagation algorithm properties (pp. 110-120)

Lecture 9:
        - Generalized linear classifiers (pp. 127-129)
        - Radial basis function networks (pp. 133-136)
        - Support vector machines: nonlinear case (pp. 139-141)

Lecture 10: 
        - Support Vector Machines: nonlinear case (pp. 142-143)
        - Feature selection (pp. 163-164)
        - Preprosessing (pp. 164-166)

Lecture 11:
        - Scatter matrices (pp. 179-182)
        - Feature generation (pp. 207-211)
        - Two special cases: chain code (pp. 298-300) and edit 
          distance (pp. 325-327)

Lecture 12:
        - Clustering (pp. 397-403)
        - Sequential clustering algorithms (pp. 429-434)

Lecture 13:
        - Proximity methods (pp. 404-413)
        - Proximity between point and set (pp. 418-421)
        - Primity between sets (pp. 423-424)

Lecture 14:
        - Hierarchical Clustering Algorithms (pp. 449-455)

Lecture 15:
        - Isodata-clustering