Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1EEE 505Introduction to Machine Learning3+0+03618.06.2026

 
Course Details
Language of Instruction English
Level of Course Unit Master's Degree
Department / Program ELECTRICAL AND ELECTRONICS ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The aim of this course is to introduce students to the fundamental concepts, methods, and application areas of machine learning. Within the scope of the course, students are expected to understand supervised and unsupervised learning approaches, as well as basic algorithms such as regression, classification, clustering, decision trees, support vector machines, and artificial neural networks. The course also aims to provide students with knowledge of data preprocessing, model selection, overfitting, generalization performance, and model evaluation, and to enable them to select and apply appropriate methods for basic machine learning problems.
Course Content Introduction to machine learning; data preprocessing and data analysis; supervised and unsupervised learning approaches; regression and classification methods; decision trees, k-nearest neighbors, Naive Bayes, support vector machines, and artificial neural networks; clustering and dimensionality reduction methods; model selection, cross-validation, overfitting and generalization; performance metrics and model evaluation; basic deep learning approaches and the application of machine learning algorithms to engineering problems.
Course Methods and Techniques The course is conducted through lectures, problem solving, applications on sample datasets, algorithm comparisons, and project/homework assignments. Classroom discussions, computer-based applications, coding examples, and model performance evaluation studies are used to help students understand machine learning algorithms. Within the scope of the course, students are expected to apply basic machine learning methods to real or sample engineering problems.
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Prof.Dr. Ergun Erçelebi ercelebi@gantep.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. 3rd Edition, O’Reilly Media. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition, Springer. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Alpaydın, E. (2020). Introduction to Machine Learning. 4th Edition, MIT Press.
Course Notes Course notes will be provided in the form of weekly lecture slides, sample problem solutions, application documents related to machine learning algorithms, and studies carried out on sample datasets. Students are expected to review the relevant weekly topic before attending the class and to have basic programming knowledge for practical studies.

Course Category
Mathematics and Basic Sciences %30
Engineering %10
Engineering Design %20
Field %10

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Assignment 5 % 20
Project 1 % 20
Final examination 1 % 30
Total
8
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Weekly lecture hours 14 3 42
Reading Activities 14 2 28
Internet browsing, library work 6 3 18
Material design, application 6 4 24
Report preparation 1 12 12
Presentation preparation 1 6 6
Presentation 1 1 1
Midterm and midterm exam preparation 1 20 20
Final exam and preparation for the final exam 1 29 29
Total Work Load   Number of ECTS Credits 6 180

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Explain the basic concepts, learning types, and main application areas of machine learning.
2 Apply basic preparation steps such as data preprocessing, feature extraction, and splitting datasets into training and test sets.
3  Select  appropriate machine learning methods for regression, classification, and clustering problems.
4 Explain and apply basic algorithms such as decision trees, k-nearest neighbors, Naive Bayes, support vector machines, and artificial neural networks.
5 Evaluate model performance using metrics such as accuracy, error rate, sensitivity, specificity, F1-score, and similar measures.
6 Compare machine learning models by using the concepts of overfitting, generalization, cross-validation, and model selection.
7 Apply basic machine learning algorithms to engineering problems and interpret the obtained results.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to machine learning, basic concepts and application areas Reviewing the course syllabus Weekly lecture notes, application documents, and the relevant chapter of the reference book.
2 Data types, datasets, train-test split and data preprocessing Reviewing basic Python and data structures Weekly lecture notes, application documents, and the relevant chapter of the reference book.
3 Supervised learning, regression problem and linear regression Reviewing regression and error concepts Weekly lecture notes, application documents, and the relevant chapter of the reference book.
4 Classification problem, k-nearest neighbors and Naive Bayes methods Reviewing classification examples Weekly lecture notes, application documents, and the relevant chapter of the reference book.
5 Decision trees and model interpretability Reading the decision tree algorithm Weekly lecture notes, application documents, and the relevant chapter of the reference book.
6 Logistic regression and linear classifiers Reviewing linear model concepts Weekly lecture notes, application documents, and the relevant chapter of the reference book.
7 Support vector machines and kernel methods Reviewing basic SVM concepts Weekly lecture notes, application documents, and the relevant chapter of the reference book.
8 Midterm exam and general review of previous topics Preparation for the midterm exam Lecture notes and study questions
9 Introduction to artificial neural networks, perceptron and multilayer networks Reviewing basic derivative and optimization concepts Weekly lecture notes, application documents, and the relevant chapter of the reference book.
10 Model training, loss functions, gradient descent and overfitting Reviewing optimization and loss functions Weekly lecture notes, application documents, and the relevant chapter of the reference book.
11 Model selection, cross-validation and performance metrics Reviewing performance metrics Haftalık ders notları, uygulama dokümanları ve ilgili referans kitap bölümü.
12 Unsupervised learning, clustering methods and k-means algorithm Reviewing clustering examples Weekly lecture notes, application documents, and the relevant chapter of the reference book.
13 Dimensionality reduction, principal component analysis and feature selection Reviewing linear algebra and covariance concepts Weekly lecture notes, application documents, and the relevant chapter of the reference book.
14 Basic deep learning approaches, project presentations and general review Preparing the project report and presentation Weekly lecture notes, application documents, and the relevant chapter of the reference book.

 
Sustainable Development Goals
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4
All 4 4 4
C1 3 2 2
C2 3 4 4
C3 4 4 4
C4 5 4 4
C5 3 5 4
C6 4 5 4
C7 5 4 5

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  https://obs.gantep.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=455331&lang=en