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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 | ||