BPP253 Machine Learning

3 ECTS - 2-0 Duration (T+A)- 3. Semester- 2 National Credit

Information

Code BPP253
Name Machine Learning
Term 2024-2025 Academic Year
Semester 3. Semester
Duration (T+A) 2-0 (T-A) (17 Week)
ECTS 3 ECTS
National Credit 2 National Credit
Teaching Language Türkçe
Level Ön Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Öğr. Gör.Dr. YILMAZ KOÇAK
Course Instructor
1


Course Goal / Objective

To provide basic knowledge about Machine Learning concepts and methods, to understand the general structure of machine learning algorithms, and to gain the ability to code machine learning algorithms with a chosen programming language (Python etc.).

Course Content

Definition and general structure of algorithm of Machine Learning, coding of machine learning algorithms with the selected programming language, regression and classification algorithms, Support Vector Machines.

Course Precondition

No

Resources

Uğuz S., Makine Öğrenmesi Teorik Yönleri ve Pyhton Uygulaması, Nobel Yayınları 2. Basım, 2021

Notes

Smola, A. and Vishwanathan, S.V.N. Introduction to Machine Learning, Yahoo Labs, Santa Clara


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Explains the concept and algorithms of Machine Learning
LO02 Defines and codes the Data Preprocessing Process
LO03 Explains regression concepts
LO04 Writes programs to solve simple and multiple linear regression problems
LO05 Explains the K-Nearest Neighbor (KNN) algorithm
LO06 Defines classification and performance metrics for classification
LO07 Explains the concepts of Support Vector Machines


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Yetkinlikler - İletişim ve Sosyal Yetkinlik Communicates effectively with all partners on a sectoral basis.
PLO02 Bilgi - Kuramsal, Olgusal has the basic knowledge necessary to develop computer software, to establish algorithm, sequential and simultaneous flow logic 4
PLO03 Yetkinlikler - Alana Özgü Yetkinlik Designs systems for fundamental problems in microcontrollers, embedded systems and analog/digital electronics.
PLO04 Yetkinlikler - Alana Özgü Yetkinlik Uses basic software related to information and communication technologies, specific to his profession. 3
PLO05 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Applies the software and hardware developments in the field of Computer Programming independently. 3
PLO06 Bilgi - Kuramsal, Olgusal Explains the necessary methods for solving well-defined problems in the field of Computer Technologies and Programming. 3
PLO07 Bilgi - Kuramsal, Olgusal Has the basic knowledge level required to develop software specific to web, mobile and other electronic platforms. 2
PLO08 Beceriler - Bilişsel, Uygulamalı Develops software for desktop and other environments. 3
PLO09 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Takes an active role in project development processes, independently or as part of a group, within a planned project.
PLO10 Bilgi - Kuramsal, Olgusal Knows project planning, development and implementation processes.
PLO11 Yetkinlikler - Alana Özgü Yetkinlik Performs data storage, editing, querying, etc. operations in computer and network environment.
PLO12 Yetkinlikler - Alana Özgü Yetkinlik It has the ability to solve unpredictable hardware and software problems.
PLO13 Beceriler - Bilişsel, Uygulamalı Codes software components that have been analyzed and the algorithm has been prepared. 3
PLO14 Bilgi - Kuramsal, Olgusal Knows the methods to be used in software development. 2
PLO15 Yetkinlikler - Öğrenme Yetkinliği Constantly follows current innovations and developments in the field of information technologies.
PLO16 Yetkinlikler - İletişim ve Sosyal Yetkinlik Communicates verbally and in writing in a foreign language.
PLO17 Yetkinlikler - İletişim ve Sosyal Yetkinlik It has the phenomenon of the necessity of moral and ethical behaviors related to the information technology profession.
PLO18 Yetkinlikler - Alana Özgü Yetkinlik Has the necessary awareness of occupational safety in her field.
PLO19 Beceriler - Bilişsel, Uygulamalı It uses operating systems with administrative features.
PLO20 Bilgi - Kuramsal, Olgusal Have basic knowledge about entrepreneurship, career management and lifelong learning.
PLO21 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Has environmental awareness, environmental sensitivity, basic knowledge about waste storage and safety.


Week Plan

Week Topic Preparation Methods
1 The importance of using Python in Machine Learning Examining Python Programming Language from source books and search engines Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
2 Installing and using Numpy/Pandas libraries Examining Python Libraries from source books and search engines. Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
3 Visualization of data with Matplotlib library Examining Data Visualization with Python. Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
4 Vectors and Matrices Examining Verctors and Matrices Öğretim Yöntemleri:
Anlatım, Soru-Cevap
5 Basic Concepts in Machine Learning Reading the subject from reference books Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
6 Application Development Processes in Machine Learning Reading the subject from reference books Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
7 Data Preprocessing Exploring the concept of Data Preprocessing Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
8 Mid-Term Exam Exam preparation Ölçme Yöntemleri:
Yazılı Sınav
9 Simple Linear Regression Exploring the concept of regression Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama
10 Multiple Linear Regression Researching regression types Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
11 Performance Benchmarks for Regression Researching performance evaluation Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
12 Bayes Theorem and Classification Exploring the concept of classification Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
13 Performance Benchmarks for Classification Investigation of classification criteria Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
14 K-Nearest Neighbor Algorithm Investigating the concept of neighborhood Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
15 Support Vector Machines Researching the subject. Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
16 Term Exams Exam preparation Ölçme Yöntemleri:
Yazılı Sınav, Ödev
17 Term Exams Exam preparation Ölçme Yöntemleri:
Yazılı Sınav, Ödev


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 2 28
Out of Class Study (Preliminary Work, Practice) 14 2 28
Assesment Related Works
Homeworks, Projects, Others 1 10 10
Mid-term Exams (Written, Oral, etc.) 1 6 6
Final Exam 1 10 10
Total Workload (Hour) 82
Total Workload / 25 (h) 3,28
ECTS 3 ECTS

Update Time: 27.09.2024 12:37