INFORMATION TECHNOLOGIES (ENGLISH, NON-THESIS)
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 Fall
Spring
3 0 3 12
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
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

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

Sources

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

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
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
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) Uses basic Software Engineering knowledge and competencies.
2) Applies the software development ability that is necessary for software engineering applications.
3) Uses data structures and applies information about algorithm development.
4) Develops system programs on operating systems.
5) Defines computer organization, design and architectures.
6) Creates the structure of computer networks and network security.
7) Uses business intelligence, data mining and data analysis tools, applies techniques about them.
8) Develops database applications and WEB based programs.
9) Defines, analyzes, designs and manages information technologies projects.
10) Uses and develops technology-based environments and tools in education.
11) Detects, identifies and solves information technology needs of the business environment.
12) Uses the capabilities of information technologies within the rules of professional responsibility and ethics.