| ENGINEERING MANAGEMENT (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP5130 | Machine Learning and Pattern Recognition | Fall | 3 | 0 | 3 | 12 |
| The course opens with the approval of the Department at the beginning of each semester |
| 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. |
|
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: | Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop |
| References: |
| 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 | |
| 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 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | - To be able to develop and deepen current and advanced knowledge in the field with original thought and/or research at the level of expertise based on master's qualifications, and to reach original definitions that will bring innovation to the field. | |
| 2) | - To be able to comprehend the interdisciplinary interaction that the field is related to; Ability to reach original results by using knowledge that requires expertise in analyzing, synthesizing and evaluating new and complex ideas. | |
| 3) | -To be able to evaluate and use new knowledge in the field with a systematic approach. | |
| 4) | - To be able to develop a new idea, method, design and/or application that brings innovation to the field, or to apply a known idea, method, design and/or application to a different field, to research, comprehend, design, adapt and apply an original subject. | |
| 5) | - Ability to critically analyze, synthesize and evaluate new and complex ideas. | |
| 6) | - Gaining high-level skills in using research methods in studies related to the field. | |
| 7) | - To be able to critically examine and develop social relations and the norms that guide these relations, and to manage actions to change them when necessary. | |
| 8) | - To be able to defend their original views in the discussion of the issues in the field with experts and to establish an effective communication showing their competence in the field. | |
| 9) | - To be able to communicate and discuss at an advanced level in written, oral and visual using a foreign language at least at the C1 General Level of the European Language Portfolio. | |
| 10) | - To be able to develop new thoughts and methods in the field by using high-level mental processes such as creative and critical thinking, problem solving and decision making. | |
| 11) | - To be able to contribute to the process of becoming an information society and maintaining it by introducing scientific, technological, social or cultural advances in the field. | |
| 12) | - To be able to interact functionally by using strategic decision-making processes in solving the problems encountered in the field. | |
| 13) | - To be able to contribute to the solution of social, scientific, cultural and ethical problems encountered in issues related to the field and to support the development of these values. | |
| 14) | - Being able to contribute to the progress in the field by independently carrying out an original work that brings innovations to the field, develops a new idea, method, design and / or application or applies a known idea, method, design and / or application to a different field. | |
| 15) | - To be able to expand the limits of knowledge in the field by publishing at least one scientific article related to the field in national and/or international refereed journals and/or by producing or interpreting an original work. | |
| 16) | - Ability to lead in environments that require the resolution of unique and interdisciplinary problems. |