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 12
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Must Course
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.

Learning Outcomes

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

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Project 5 % 10
Midterms 1 % 40
Final 1 % 50
Total % 100
Total % 100

ECTS / Workload Table

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

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) 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) Demonstrate professional and ethical responsibility.