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Information
| Unit | FACULTY OF ENGINEERING |
| INDUSTRIAL ENGINEERING PR. | |
| Code | ENS310 |
| Name | Introduction to Machine Learning with Pythonn |
| Term | 2026-2027 Academic Year |
| Semester | 6. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 4 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. ALİ KOKANGÜL |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of the course is to provide students with knowledge about the basic methods in the field of artificial intelligence and to enable students to use artificial intelligence methods in solving practical problems.
Course Content
Python data structures, Numpy array operations, Data analysis applications with Pandas library, Regression and classification applications with machine learning models,
Course Precondition
None
Resources
Géron, A. (2021). Scikit-Learn, keras ve tensorflow ile uygulamalı makine öğrenmesi (1. baskı). B. Aksoy, ve Ö. Kaya, Çev.). Buzdağı Yayınevi.(Orijinal eserin basım tarihi 2019).
Notes
Deep Learning, I. Goodfellow, Y.Bengio, A.Courville
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learning the basic libraries of the Python programming language used in Machine Learning. |
| LO02 | Learning to analyze and process data with the Python programming language. |
| LO03 | Understanding the working principles of algorithms used in machine learning. |
| LO04 | Learning how to analyze model outputs and adjust model parameters based on the results. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Acquires sufficient knowledge in mathematics, science and Industrial Engineering discipline-specific subjects; acquires the ability to use theoretical and applied knowledge in these fields in complex engineering problems. | 2 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Acquires the ability to identify, define, formulate and analytically solve complex Industrial Engineering problems; and has the ability to select and apply appropriate analysis and modelling methods for this purpose. | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Acquires the ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; acquires the ability to apply modern design methods for this purpose. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Acquires the ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in Industrial Engineering applications and acquires the ability to use information technologies effectively. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Acquire the skills to design and conduct experiments, collect data, analyze and interpret results to investigate complex Industrial Engineering problems or discipline-specific research topics. | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Have the ability to work effectively in disciplinary and multi-disciplinary teams or individually. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Ability to communicate effectively in Turkish, both verbally and in writing; knowledge of at least one foreign language; ability to write and understand effective reports, prepare design and production reports, make effective presentations, and give and receive clear and understandable instructions. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | They have awareness of the necessity of lifelong learning; they have the ability to access information, follow developments in science and technology, and constantly renew themselves. | 3 |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Acting in accordance with ethical principles, becoming knowledgeable about the standards used in engineering practices with awareness of professional and ethical responsibility. | |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Learn about business practices such as project management, risk management and change management, and is aware of entrepreneurship and innovation. | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Informed about the universal and societal impacts of engineering practices on health, environment and safety, and the contemporary problems reflected in the field of engineering, and is aware of the legal consequences of engineering solutions. | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Benefit from the power of effective communication in professional life and has the ability to interpret developments correctly. | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Have the ability to design, develop, implement and improve integrated systems that include machines, people, time, information or money. | |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | By applying modern design methods, they have the ability to design, develop, implement and improve complex products, processes, businesses and systems under realistic conditions and constraints such as cost, environment, sustainable development, energy, manufacturability, ethics, health, safety and political issues. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Python Basics | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 2 | Numpy Library | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 3 | Pandas Library, Matplotlib Library | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Data preparation, cleaning, and processing. | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 5 | Machine learning regression models (Linear-Multiple-Polynomial Regression) | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 6 | Machine learning regression models (Decision Tree Regression, Random Forest Regression) | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 7 | Machine learning regression models (XgBoost) | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Reading and reviewing lecture notes and textbooks. | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Machine learning classification models (K-Neirest Neighbour (KNN), Support Vector Machine (SVM)) | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 10 | Machine learning classification models modelleri (Naive Bayes, Decision Tree, Random Forest) | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 11 | Evaluation metrics and error functions | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | Hyperparameter Optimization | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Logistic Regression | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Artificial Neural Networks | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Artificial Neural Networks II | Reading lecture notes and references about the subject | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Term Exams | Reading and reviewing lecture notes and textbooks. | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Reading and reviewing lecture notes and textbooks. | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 8 | 8 |
| Final Exam | 1 | 10 | 10 |
| Total Workload (Hour) | 88 | ||
| Total Workload / 25 (h) | 3,52 | ||
| ECTS | 4 ECTS | ||