MAT5020 Biostatistics MethodsBahçeşehir UniversityDegree Programs APPLIED MATHEMATICS (TURKISH, THESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
APPLIED MATHEMATICS (TURKISH, THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
MAT5020 Biostatistics Methods Fall 3 0 3 12
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: Turkish
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Prof. Dr. CANAN ÇELİK KARAASLANLI
Recommended Optional Program Components: Matlab
Course Objectives: The objective of this course is to provide students with theory, methods and practice in data mining, inference, prediction and information in computational biology, medicine, bioinformatics,biotechnology.

Learning Outcomes

The students who have succeeded in this course;
At the end of the course students should have a good overview of modern methods in statistical learning. They should also be able to choose and, by calculation and simulation, work them out appropriately in contexts of applications.

Course Content

Various methods from statistics,discrete mathematics, numerical analysis and information theory are presented and combined from the view-point of modern algorithms and applications.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction into statistical learning
2) Introduction into supervised learning
3) Linear methods of regression and Applications in Matlab
4) Linear Regression
5) Linear Methods of Classification
6) Linear methods in classification, and Model assessment and selection
7) Model assessment and selection
8) Model inference and averaging
9) Model infer. & aver., and Additive models and trees
10) Additive models and trees
11) Prototype methods and nearest neighbours
12) Prot. Meth. & n. neighb., and Cluster algor. & support vector machines
13) Unsupervised Learning and term projects
14) Presentations of Term Projects

Sources

Course Notes / Textbooks: D.T. Hastie, R. Tibshirani and J. Friedman, “The Elemenents of Statistical Learning”, Springer Series in Statistics, 2001
References: N. Christianini and J. Shawe-Taylor, “An Introduction to Support Vector
Machines”, Cambridge University Press, 2000.
E. Alpaydin, Introduction to Machine Learning, 2e, The MIT Press, February 2010,
ISBN-10: 0-262-01243-X ISBN-13: 978-0-262-01243-0
E. Alpaydin, Yapay Öğrenme, Boğaziçi Üniversitesi Yayınevi, April 2011, ISBN: 978-6-054-23849-1

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance 14 % 0
Laboratory 10 % 0
Homework Assignments 3 % 15
Project 1 % 20
Midterms 1 % 25
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 2 28
Laboratory 14 1 14
Study Hours Out of Class 14 3 42
Presentations / Seminar 1 15 15
Project 1 25 25
Homework Assignments 3 5 15
Midterms 1 25 25
Final 1 36 36
Total Workload 200

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Ability to assimilate mathematic related concepts and associate these concepts with each other.
2) Ability to gain qualifications based on basic mathematical skills, problem solving, reasoning, association and generalization. 2
3) Be able to organize events, for the development of critical and creative thinking and problem solving skills, by using appropriate methods and techniques. 3
4) Ability to make individual and team work on issues related to working and social life. 2
5) Ability to transfer ideas and suggestions, related to topics about his/her field of interest, written and verball. 3
6) Ability to use mathematical knowledge in technology. 3
7) To apply mathematical principles to real world problems.
8) Ability to use the approaches and knowledge of other disciplines in Mathematics. 3
9) Be able to set up and develope a solution method for a problem in mathematics independently, be able to solve and evaluate the results and to apply them if necessary.
10) To be able to link abstract thought that one has to concrete events and to transfer the solutions and examine and interpret the results scientifically by forming experiments and collecting data. 3
11) To be able to conduct a research either as an individual or as a team member, and to be effective in each related step of the project, to take role in the decision process, to plan and manage the project by using time effectively.
12) To be able to acquire necessary information and to make modeling in any field that mathematics is used and to improve herself/himself,