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COURSE UNIT TITLECOURSE UNIT CODESEMESTERTHEORY + PRACTICE (Hour)ECTS
INTELLIGENT DATA ANALYSIS BİL622 Program Course List-------- 3 + 0 10

TYPE OF COURSE UNITElective Course
LEVEL OF COURSE UNITDoctorate Of Science
YEAR OF STUDY-
SEMESTERProgram Course List--------
NUMBER OF ECTS CREDITS ALLOCATED10
NAME OF LECTURER(S)-
LEARNING OUTCOMES OF THE COURSE UNIT At the end of this course, the students;
1) Learn basic principles of statistical data analysis
2) Get practice on developing and using PR programs
3) Get ability to PR techniques in problem solving
MODE OF DELIVERYFace to face
PRE-REQUISITES OF THE COURSENo
RECOMMENDED OPTIONAL PROGRAMME COMPONENTNone
COURSE CONTENTS
WEEKTOPICS
1st Week Data analysis fundamentals
2nd Week Feature Reduction
3rd Week Supervised classification
4th Week Perceptron Algorithms
5th Week Linear Discriminants
6th Week Nearest Neigborhood
7th Week Maximum Likelihood Estimation,Bayesian inference
8th Week Mid-term
9th Week Suppert Vector Machines
10th Week Hidden Markov Models
11th Week Unsupervised methods
12th Week K-means
13th Week Hierarchical clustering
14th Week Recent challenges
RECOMENDED OR REQUIRED READINGPattern Classification 2nd. Edition., R.O. Duda, P.E. Hart & D.G. Stork, J. Wiley Inc., 2001
PLANNED LEARNING ACTIVITIES AND TEACHING METHODSProject
ASSESSMENT METHODS AND CRITERIA
 QuantityPercentage(%)
Mid-term130
Project130
Total(%)60
Contribution of In-term Studies to Overall Grade(%)60
Contribution of Final Examination to Overall Grade(%)40
Total(%)100
LANGUAGE OF INSTRUCTIONTurkish
WORK PLACEMENT(S)No
  

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