CYBER SECURITY (ENGLISH, THESIS) | |||||
Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
CMP5130 | Machine Learning and Pattern Recognition | Spring 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: | English |
Type of course: | Departmental Elective |
Course Level: | |
Mode of Delivery: | Face to face |
Course Coordinator : | Assist. Prof. CEMAL OKAN ŞAKAR |
Course Lecturer(s): |
Assist. Prof. 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. |
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 |
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 The teaching methods of the course include lectures, group work, technology-assisted learning, project preparation, and practice. |
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 |
Course Notes / Textbooks: | Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop |
References: |
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 |
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 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Understand and implement advanced concepts of Siber Security | |
2) | Use math, science, and modern engineering tools to formulate and solve advenced siber security problems. | |
3) | Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results | |
4) | Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study | |
5) | Work effectively in multi-disciplinary research teams | |
6) | Acquire scientific knowledge | |
7) | Find out new methods to improve his/her knowledge. | |
8) | Effectively express his/her research ideas and findings both orally and in writing | |
9) | Defend research outcomes at seminars and conferences. | |
10) | Prepare master thesis and articles about thesis subject clearly on the basis of published documents, thesis, etc. | |
11) | Demonstrate professional and ethical responsibility. |