| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Learn basic principles of pattern recognition
- Learn machine learning methods
- Get practice on developing and using PR programs
- Get ability to PR techniques in probelm solving
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | An introduction to the machine recognition of one, two or higher dimensional patterns. Statistical and linguistic approaches. Survey of application areas. Bayes Decision Theory, decision bounderies, classifiers and discriminant functions. Estimation of parameters. Clustering. Feature selection. Structural approaches to pattern recognition. Neural network recognizers. Applications. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to Pattern Recognition |
|
2nd Week |
Data and Feature Fundamentals |
|
3rd Week |
Data Preprocessing and Normalization |
|
4th Week |
Feature Extraction |
|
5th Week |
Feature Selection, Instance Segmentation |
|
6th Week |
Fundamentals of Clustering |
|
7th Week |
Fundamentals of Classification |
|
8th Week |
MIDTERM EXAM |
|
9th Week |
Fundamentals of Regression |
|
10th Week |
Performance Evaluation Metrics |
|
11th Week |
Linear Classifiers |
|
12th Week |
Artificial Neural Networks - I |
|
13th Week |
Artificial Neural Networks - II |
|
14th Week |
Deep Learning |
|
15th Week |
Deep Learning with Matlab |
|
| Recommended or Required Reading | 1. Pattern Classification 2nd. Edition., R.O. Duda, P.E. Hart & D.G. Stork, J. Wiley Inc., 2001 |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Problem Solving
- Experiment
- Practice
- Project
- Report Preparation
- Presentation
- Other
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
1 |
10 |
| Quiz |
3 |
10 |
| Project |
1 |
10 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1.5 |
1.5 |
| Preparation for Quiz |
4 |
1 |
4 |
| Individual or group work |
|
|
|
| Preparation for Final exam |
1 |
30 |
30 |
| Course hours |
14 |
4 |
56 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
3 |
10 |
30 |
| Quiz |
4 |
0.5 |
2 |
| Total Work Load | 145.5 |
| Total Work Load / 30 (h) | 4.85 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
