APPLIED MATHEMATICS (TURKISH, THESIS) | |||||
Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 |
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. |
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. |
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. |
Various methods from statistics,discrete mathematics, numerical analysis and information theory are presented and combined from the view-point of modern algorithms and applications. |
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 |
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 |
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 |
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 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution |