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FIRST CYCLE - BACHELOR'S DEGREE
FACULTY OF ENGINEERING
INDUSTRIAL ENGINEERING DEPARTMENT
255 Industrial Engineering
Course Information
Course Learning Outcomes
Course's Contribution To Program
ECTS Workload
Course Details
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COURSE INFORMATION
Course Code
Course Title
L+P Hour
Semester
ECTS
MTH 114
RECOMMENDATION SYSTEMS WITH ARTIFICIAL INTELLIGENCE
3 + 0
7th Semester
5
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
1
Will be able to learn and use the methods and algorithms used in artificial intelligence and recommendation systems.
2
Gain the ability to produce solutions to the problems encountered with appropriate artificial intelligence methods.
3
Will be able to apply data preprocessing techniques.
4
Will be able to develop an end-to-end recommendation system with deep learning.
COURSE'S CONTRIBUTION TO PROGRAM
PO 01
PO 02
PO 03
PO 04
PO 05
PO 06
PO 07
PO 08
PO 09
PO 10
PO 11
PO 12
PO 13
LO 001
LO 002
LO 003
LO 004
Sub Total
Contribution
0
0
0
0
0
0
0
0
0
0
0
0
0
ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities
Quantity
Duration (Hour)
Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)
14
3
42
Assignments
4
6
24
Mid-terms
1
10
10
Final examination
1
14
14
Presentation / Seminar Preparation
1
14
14
Report / Project
1
26
26
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
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Home Page
About University
Name And Address
Acedemic Authorities
General Discription
Academic Calendar
General Admission Requirements
Recognition of Prior Learning
General Registration Procedures
ECTS Credit Allocation
Academic Guidance
Information For Students
Cost Of Living
Accommodation
Meals
Medical Facilities
Facilities for Special Needs Students
Insurance
Financial Support for Students
Student Affairs
Learning Facilities
International Programs
Language Courses
Internships
Sports Facilities and Leisure Activities
Student Associations
Practical Information for Mobile Students
Degree Programmes