CYBER SECURITY (ENGLISH, NONTHESIS) | |||||
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 | 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 : | 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. |
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 |
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) | 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 |