CMP5130 Machine Learning and Pattern RecognitionBahçeşehir UniversityDegree Programs ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NONTHESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NONTHESIS)
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: 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.

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

Evaluation System

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

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