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 Spring 3 0 3 6
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
8.Optimal Partitioning of Sparse Similarities Using Metis
10.Jarvis-Patris Clustering Algorithm
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


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

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
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) 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.