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

Learning outcomes

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

1. Differentiate between useful and useless information in large data stack in Bioinformatics.

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology
   2. Link the concepts belonging to the different disciplines and interpret & analyze scientific research in these disciplines.

   Method of assessment

   1. Written exam
   2. Homework assignment
2. Detect information, pattern, and rules hidden in large data stack in Bioinformatics.

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Seminar/presentation
3. Analyze the performance of the artificial intelligence methods and interpret the outcomes of these methods.

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology

   Method of assessment

   1. Homework assignment
4. List the steps for extraction of useful information in the data

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology

   Method of assessment

   1. Written exam
   2. Homework assignment
5. List the fundamental statistical techniques used in NLP.

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology

   Method of assessment

   1. Written exam
   2. Homework assignment
6. Apply known NLP techniques to real world problems.

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology

   Method of assessment

   1. Written exam
   2. Homework assignment
7. Understanding optimization techniques in Bioinformatics

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology

   Method of assessment

   1. Written exam
   2. Homework assignment

Contents
Week 1:	Introduction
Week 2:	Data, Exploration
Week 3:	Search and Inference in AI
Week 4:	Classification techniques
Week 5:	Clustering techniques
Week 6:	Association Rules, Dimention Reduction Techniques
Week 7:	Midterm exam
Week 8:	Introduction to Optimization Methods for Bioinformatics, Heuristic Optimization
Week 9:	Optimization Methods II: Genetic Algorithms in Bioinformatics
Week 10:	Vector Semantics
Week 11:	Language Models
Week 12:	Naural Language Processing Techniques
Week 13:	Applications to the bioinformatics (paper presentations)
Week 14:	Applications to the bioinformatics (paper presentations)
Week 15*:	-
Week 16*:	-
Textbooks and materials:	Data Mining: Concepts and Techniques, 3rd Edition, By Han, Jiawei, Kamber, Micheline, 2012. Artificial Intelligence: A Modern Approach, 3rd Edition, Stuart Russell, Peter Norvig, 2010. Speech and Language Processing, 3rd Edition, Daniel Jurafsky, James H. Martin, Prentice Hall, 2017
Recommended readings:	yok
	* 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:	7	30
Other in-term studies:		0
Project:		0
Homework:	3,6,9,12	35
Quiz:		0
Final exam:	16	35
	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:	8	8
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	12	1
Mid-term:	1	1
Personal studies for final exam:	20	1
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)