MECHATRONICS ENGINEERING (ENGLISH, NON-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 | 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) | Gains an academic background and abilities for making scientific research; analysis, interpretation and application of knowledge in subjects of Mechatronics Engineering. | |
2) | Acquires an ability to select, apply and develop modern techniques and methods for mechatronics engineering applications. | |
3) | Develops new and innovative ideas, procedures and solutions in the design of mechatronics systems, components and processes. | |
4) | Gains an ability for experimental design, data accumulation, data analysis, reporting and implementation. | |
5) | Acquires abilities for individual and team-work, communication and collaboration with team members and interdisciplinary cooperation. | |
6) | Gains an ability to communicate effectively oral and written; and a knowledge of English sufficient to follow technical developments and terminology. | |
7) | Acquires recognition of the need for, and an ability to access and report knowledge, to engage in life-long learning. | |
8) | Gains an understanding of universal, social and professional ethics. | |
9) | Acquires a knowledge of business-oriented project organization and management; awareness of entrepreneurship, innovation and sustainable development | |
10) | Gains awareness for the impact of mechatronics engineering applications on human health, environmental, security and legal issues in a global and social context. |