Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
8BLMS417Machine Learning3+0+03523.02.2026

 
Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Computer Engineering
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The objective of this course is to introduce students to the fundamental concepts, methods, and applications of machine learning; to equip them with a data-driven problem-solving approach. Students will learn statistical learning theory, supervised and unsupervised learning algorithms, model evaluation methods, and modern machine learning applications, thereby gaining the ability to develop solutions for real-world problems.
Course Content The course will cover an introduction to machine learning, fundamental concepts and terminology, linear models (regression, classification), decision trees, clustering methods, dimensionality reduction, model evaluation and optimization techniques, an introduction to artificial neural networks, and approaches that form the basis for deep learning.
Course Methods and Techniques Lecture, question and answer, computer application
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof. Başak BULUZ KÖMEÇOĞLU
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer.
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press.
Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly.
Ethem Alpaydın, Artificial Learning, Boğaziçi University Press.
Course Notes Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer.
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press.
Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly.
Ethem Alpaydın, Artificial Learning, Boğaziçi University Publications.
Textbooks and documents to be uploaded to university information systems.

Course Category
Mathematics and Basic Sciences %35
Engineering %35
Engineering Design %30
Social Sciences %0
Education %0
Science %0
Health %0
Field %0

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 % 35
Assignment 1 % 10
Project 1 % 25
Final examination 1 % 30
Total
4
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 15 5 75
Presentation 1 1 1
Mid-terms 1 2 2
Project 1 1 1
Final examination 1 2 2
Total Work Load   Number of ECTS Credits 5 123

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 They will be able to explain the basic concepts of machine learning, its types (supervised, unsupervised, reinforcement), and its application areas.
2 It will be able to apply fundamental algorithms such as linear regression, logistic regression, decision trees, clustering, and dimensionality reduction.
3 The performance of learning algorithms can be evaluated using appropriate metrics (accuracy, error rate, F1 score, etc.).
4 They will be able to recognize overfitting and underfitting problems and apply solution strategies (regularization, cross-validation, etc.).
5 Data preprocessing, modeling, and result analysis can be performed using Python libraries.
6 Small-scale machine learning projects can be developed using real-world data.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Machine Learning and Basic Concepts
2 Data Preprocessing and Feature Engineering
3 Linear Regression
4 Logistic Regression and Classification
5 Model Evaluation
6 Decision Trees and Ensemble Methods
7 Support Vector Machines (SVM)
8 Midterm Exam
9 Clustering Methods
10 Dimension Reduction
11 Introduction to Artificial Neural Networks
12 Training Process
13 Standardization and General Challenges
14 Introduction to Deep Learning
15 Deep Learning Applications

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
C1 1
C2 5
C3 3 5
C4 3 5
C5 3 3 5
C6 3 5 2

  bbb

  
  https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=243266&curProgID=5607&lang=en