• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Use the specialized methods for mining of biological data based on its type and structure

   Contribution to Program Outcomes

   1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
   2. Apply mathematical analysis and modeling methods for bioengineering design and production processes.
   3. Develop an awareness of continuous learning in relation with modern technology.

   Method of assessment

   1. Written exam
   2. Seminar/presentation
2. Use machine learning techniques for modelling the biological data

   Contribution to Program Outcomes

   1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
   2. Apply mathematical analysis and modeling methods for bioengineering design and production processes.
   3. Develop an awareness of continuous learning in relation with modern technology.

   Method of assessment

   1. Written exam
   2. Seminar/presentation
3. Use the deep learning techniques in modelling of biological data

   Contribution to Program Outcomes

   1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
   2. Apply mathematical analysis and modeling methods for bioengineering design and production processes.

   Method of assessment

   1. Written exam
   2. Seminar/presentation

Contents
Week 1:	Types of Biological Data: Omics Data
Week 2:	Types of Biological Data: Clinical Data
Week 3:	Unsupervised Classification Methods
Week 4:	Supervised Classification Methods
Week 5:	Unsupervised Clustering Methods
Week 6:	Supervised Clustering Methods
Week 7:	Algorithms for Learning Association Rules
Week 8:	Detection of Outliers Midterm Exam
Week 9:	Text Mining
Week 10:	Data Mining Methods for Clinical data
Week 11:	Data Mining Methods for Time-course data
Week 12:	Machine learning methods for biological data
Week 13:	Deep learning methods for biological data
Week 14:	Commercial uses of data mining Student presentations
Week 15*:	-
Week 16*:	Final exam
Textbooks and materials:	Data Mining: Concepts and Techniques, By Han, Jiawei, Kamber, Micheline, Morgan Kaufmann, 3rd ed., 2016.
Recommended readings:	Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hand , Heikki Mannila , Padhraic Smyth, 2001. Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) by Ian H. Witten, Eibe Frank, 2016 Data Mining Solutions Methods and Tools for Solving Real World Problems, Christopher Westphal, Teresa Blaxton, Wiley, 1998 Kavram ve Algoritmalarıyla Temel Veri Madenciliği, Dr. Gökhan Silahtaroğlu, Birinci Baskı, Papatya Yayınları, 2018
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	8	30
Other in-term studies:	14	30
Project:		0
Homework:		0
Quiz:		0
Final exam:	16	40
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	3	14
Practice, Recitation:	0	0
Homework:	0	0
Term project:	1	14
Term project presentation:	1	1
Quiz:	0	0
Own study for mid-term exam:	3	3
Mid-term:	2	1
Personal studies for final exam:	3	3
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)