CYBER SECURITY (ENGLISH, 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
CMP5130 Machine Learning and Pattern Recognition Fall 3 0 3 7
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
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
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 Outputs

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

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments % 0
Presentation % 0
Project 5 % 10
Seminar % 0
Midterms 1 % 40
Preliminary Jury % 0
Final 1 % 50
Paper Submission % 0
Jury % 0
Bütünleme % 0
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
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 14 6 84
Presentations / Seminar 0 0 0
Project 5 5 25
Homework Assignments 0 0 0
Quizzes 0 0 0
Preliminary Jury 0
Midterms 1 20 20
Paper Submission 0
Jury 0
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.
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.
1) Being able to independently carry out a work that requires expertise in the field.
1) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
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.
2) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines,
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.
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.
2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
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.
2) To be able to comprehend the interdisciplinary interaction with which the field is related.
3) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.
3) Being able to lead in environments that require the resolution of problems related to the field.
3) To be able to solve the problems encountered in the field by using research methods.