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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of ELECTRICAL and ELECTRONICS ENGINEERING
Masters with Thesis
Course Catalog
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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of ELECTRICAL and ELECTRONICS ENGINEERING / Masters with Thesis
Katalog Ana Sayfa
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ELKI5970Pattern Recognition and Machine Learning3+0+0ECTS:7.5
Year / SemesterFall Semester
Level of CourseSecond Cycle
Status Elective
DepartmentDEPARTMENT of ELECTRICAL and ELECTRONICS ENGINEERING
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 3 hours of lectures per week
LecturerProf. Dr. Temel KAYIKÇIOĞLU
Co-Lecturer
Language of instruction
Professional practise ( internship ) None
 
The aim of the course:
The aim of this course is to provide information on mathematical fundamentals of Pattern Recognition and Machine Learning.
 
Programme OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
PO - 1 : To describe main steps of Pattern Recognition and Machine Learning.1 - 2 - 51,2
PO - 2 : To realize training and test procedures.1 - 2 - 51,2,3
PO - 3 : To describe the classification algorithms.1 - 2 - 51,2
PO - 4 : To describe the clustering algoritms. 1 - 2 - 51,2
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome

 
Contents of the Course
Introduction, Feature Generation, K-NN Classifier, Training and Testing, Decision Theory, Bayesian Classifier, Linear Classifiers, Nonlinear Classifiers, Feature Selection, Data Transformation, Dimensionality Reduction, Decision Trees, Clustering Algorithms,Ensemble learning algorithms , Regression algorithms.
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Introduction
 Week 2Feature Generation
 Week 3K-NN Classifier, Training and Testing
 Week 4Decision Theory
 Week 5Bayesian Classifier,
 Week 6Linear Classifiers
 Week 7Nonlinear Classifiers
 Week 8Feature Selection
 Week 9Midterm Exam
 Week 10Data Transformation
 Week 11Dimensionality Reduction
 Week 12Decision Trees
 Week 13
 Week 14Ensemble learning algorithms
 Week 15Regression algorithms
 Week 16End-of-term exam
 
Textbook / Material
1Lecture Notes
 
Recommended Reading
1 Christopher, Bishop 2006, Pattern Recognition and Machine Learning,
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 2,00 30
Project 15 1,0 20
End-of-term exam 16 1,5 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 2 14 28
Arasınav için hazırlık 3 7 21
Arasınav 2 1 2
Proje 6 13 78
Dönem sonu sınavı için hazırlık 3 7 21
Dönem sonu sınavı 2 1 2
Total work load194