CYBER SECURITY (ENGLISH, NONTHESIS)
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
CMP5130	Machine Learning and Pattern Recognition	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:	English
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
Course Lecturer(s):	Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
Recommended Optional Program Components:	None
Course Objectives:	Pattern recognition systems and components; decision theories and classification; discriminant functions; supervised and unsupervised training; clustering; feature extraction and dimensional reduction; sequential and hierarchical classification; applications of training, feature extraction, and decision rules to engineering problems.

Learning Outcomes

The students who have succeeded in this course;
I. Understand the nature and inherent difficulties of the pattern recognition problems
II. Understand concepts, trade-offs, and appropriateness of the different feature types and classification techniques such as Bayesian, maximum-likelihood, etc.
III. Select a suitable classification process, features, and proper classifier to address a desired pattern recognition problem.
IV. Demonstrate algorithm implementation skills using available resources and be able to properly interpret and communicate the results clearly and concisely using pattern recognition terminology
V. Understand the mathematical statistics foundations of the pattern recognition algorithms
VI. Evaluate current research and advanced topics in pattern recognition

Course Content

1.Density Based Clustering
2.Agglomerative Clustering
3.Cluster Evaluation
4.Cohesion, Separation, Cluster Tendency
5.Prototoype-Based Clustering
6.Fuzzy Clustering
7.Sparsification
8.Optimal Partitioning of Sparse Similarities Using Metis
9.Chamelon
10.Jarvis-Patris Clustering Algorithm
11.BIRCH
12.CURE
13.Combining Multiple Clusterings

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Overview and Decision Trees	None
2)	Probability Review	None
3)	Instance-based Learning	None
4)	Naive Bayes	None
5)	Logistic Regression	None
6)	Linear Regression	None
8)	Neural Networks	None
9)	Midterm 1	Review all the topics
10)	Model Selection	None
11)	K-means and Hierarchical Clustering	None
12)	Probabilistic Models for Clustering	None
13)	Semi-Supervised Learning	None
14)	Reinforcement Learning	None

Sources

Course Notes / Textbooks:	Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop
References:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	5	% 10
Midterms	1	% 40
Final	1	% 50
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	3	42
Study Hours Out of Class	14	6	84
Project	5	5	25
Midterms	1	20	20
Final	1	20	20
Total Workload			191

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)	Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.	4
1)	Being able to independently carry out a work that requires expertise in the field.	4
1)	To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.	4
1)	To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.	4
1)	To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines,	4
1)	To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.	4
2)	To be able to comprehend the interdisciplinary interaction with which the field is related.	5
2)	To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.	5
2)	To be able to critically examine social relations and the norms that guide these relations, to develop them and take action to change them when necessary.	5
2)	To be able to develop strategy, policy and implementation plans in the fields related to the field and to evaluate the obtained results within the framework of quality processes.	5
2)	To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.	5
3)	To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.	5
3)	Being able to lead in environments that require the resolution of problems related to the field.	5
3)	To be able to solve the problems encountered in the field by using research methods.	5