Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
8MCTS431Machine Learning3+0+036

Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program Mechatronics Engineering (English)
Mode of Delivery Face to Face
Type of Course Unit Elective
Objectives of the Course The aim of the course is to teach the theoretical subjects of Machine Learning together with application examples in different fields.
Course Content Introduction, Decision Trees, Example Based Learning, Bayesian Learning, Logistic Regression, Neural Networks, Support Vector Machines, Model Selection, Feature Selection, Clustering, k-means, Maximum Expectation, Gaussian Mixture Model, Ensemble Learning, Competitive Learning, Deep Learning, Learning with Reward-Punishment
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Hikmet Canlı
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources

Course Category
Mathematics and Basic Sciences %40
Engineering %40
Engineering Design %30

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 % 40
Final examination 1 % 60
Total
2
% 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 14 3 42
Assignments 2 10 20
Presentation 1 5 5
Mid-terms 1 1 1
Practice 2 5 10
Final examination 1 1 1
Total Work Load   Number of ECTS Credits 4 121

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 The student understands the basics of machine learning.
2 The student learns well-known instructor, no instructor, and semi-instructor learning algorithms.
3 The student can apply machine learning techniques to real world problems.


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to machine learning, Supervised, unsupervised, semi-supervised
2 Learning by example
3 Multivariate models and regression
4 Multivariate models and regression
5 Artificial neural networks
6 Artificial neural networks
7 Artificial neural networks
8 Midterm Exam
9 Clustering algorithms
10 Clustering algorithms
11 Support vector machines
12 Decision trees
13 Dimensional reduction and principal component analysis.
14 deep learning and auto coders


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 4 4 4 4 4 4 4 4 4 4
C1 4 4 4 4 4 4 4 4 4 4
C2 4 4 4 4 4 4 4 4 4 4
C3 4 4 4 4 4 4 4 4 4 4

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https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=208042&curProgID=5596&lang=en