1. FIRST CYCLE - BACHELOR'S DEGREE
  2. FACULTY OF ENGINEERING
  3. INDUSTRIAL ENGINEERING DEPARTMENT
  4. 255 Industrial Engineering
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
MTH 114RECOMMENDATION SYSTEMS WITH ARTIFICIAL INTELLIGENCE3 + 07th Semester5

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective The aim of this course is to teach students the methods and algorithms used in the field of artificial intelligence and recommender systems and to provide them with the ability to use this knowledge in practice. Starting from the basics of machine learning, students will learn topics such as linear regression, classification, neural networks, content-based and collaborative recommender systems. In addition, data pre-processing techniques, the ability to develop end-to-end recommender systems with deep learning and MLOps concepts will be covered. At the end of the course, students will discuss the problems they faced through projects and complete their projects with mentoring.
Course Content This course provides a comprehensive introduction to artificial intelligence and recommendation systems. At the beginning of the course, topics such as linear regression, classification problems and gradient descent will be covered, focusing on basic machine learning concepts. Practical topics such as data splitting, selection and regularization of features will also be covered in this chapter. In the neural networks section, basic neural network structures such as activation functions and multilayer perceptrons will be examined. The rest of the course will focus on recommendation systems. First, the architecture of content-based recommendation systems will be discussed in detail, focusing on the taxonomy of recommendation systems, similarity criteria and success metrics. Alternative approaches such as collaborative recommendation systems and user-item-based nearest neighbor recommendations will also be examined. Hybrid recommendation systems will form an important part of combining different approaches. In the data preprocessing techniques section, practical issues such as data cleaning, preparation for model feeding, and data engineering will be discussed. There will also be an in-depth review on the design and implementation of end-to-end recommendation systems with deep learning. Detailed information on the concept and architecture of MLOps and how it can be integrated into recommendation systems will be presented. In the final sections of the course, performance criteria of recommendation systems, basic problems (such as cold start, sparsity) and special application areas of deep learning and recommendation systems will be discussed. Additionally, students will have the opportunity to put into practice what they have learned through projects and will complete these projects under mentoring. Reporting and presentation of projects will also form part of the course.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Will be able to learn and use the methods and algorithms used in artificial intelligence and recommendation systems.
2Gain the ability to produce solutions to the problems encountered with appropriate artificial intelligence methods.
3Will be able to apply data preprocessing techniques.
4Will be able to develop an end-to-end recommendation system with deep learning.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12PO 13
LO 001             
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Sub Total             
Contribution0000000000000

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Assignments4624
Mid-terms11010
Final examination11414
Presentation / Seminar Preparation11414
Report / Project12626
Total Work Load

ECTS Credit of the Course





130

5
COURSE DETAILS
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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes