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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of MOLECULAR BIOLOGY and GENETICS
MOLECULAR BIOLOGY and GENETICS Master Program
Course Catalog
http://www.ktu.edu.tr/molekulerb
Phone: +90 0462 377 3686
FBE
GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of MOLECULAR BIOLOGY and GENETICS / MOLECULAR BIOLOGY and GENETICS Master Program
Katalog Ana Sayfa
  Katalog Ana Sayfa  KTÜ Ana Sayfa   Katalog Ana Sayfa
 
 
MBGL5049Industrial Biotechnology: Microorganisms3+0+0ECTS:7.5
Year / SemesterFall Semester
Level of CourseSecond Cycle
Status Elective
DepartmentDEPARTMENT of MOLECULAR BIOLOGY and GENETICS
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 3 hours of lectures per week
Lecturer--
Co-Lecturer
Language of instruction
Professional practise ( internship ) None
 
The aim of the course:
To get the ability of the use of machine learning techniques with Phyton in the analysis of health data.
 
Programme OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
PO - 1 : Ability to cluster health data with Python
PO - 2 : Ability to classify health data with Python
PO - 3 : To be able to realize KNN, linear regression methods on health data with Python
PO - 4 : To be able to implement Naive Bayes classifier on health data with Python
PO - 5 : To be able to implement Neural Networks and SVM methods on health data with Python
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
Basic knowledge of Python programming language, KNN classification, Linear regression, Naive Bayes classifier, Neural Networks, SVM, clustering and applications with Phyton
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Phyton programming language basic information
 Week 2Phyton programlama dili temel bilgiler
 Week 3KNN classification with Phyton
 Week 4KNN classification with Phyton
 Week 5Linear regression method implementation with Phyton
 Week 6Linear regression method implementation with Phyton
 Week 7Mid-term examination
 Week 8Naive Bayes classification with Phyton
 Week 9Naive Bayes classification with Phyton
 Week 10Implementation of Neural Networks with Phyton
 Week 11Implementation of Neural Networks with Phyton
 Week 12Implementation of SVM method with Phyton
 Week 13Implementation of SVM method with Phyton
 Week 14Clustering with Phyton
 Week 15Clustering with Phyton
 Week 16Final examination
 
Textbook / Material
 
Recommended Reading
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)

    

    

    

    

    

 
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 4 14 56
Sınıf dışı çalışma 5 14 70
Arasınav için hazırlık 2 6 12
Arasınav 2 1 2
Ödev 3 14 42
Dönem sonu sınavı için hazırlık 2 16 32
Dönem sonu sınavı 2 1 2
Total work load216