PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
CENG 588	DEEP LEARNING AND APPLICATIONS	3 + 0	1st Semester	7,5

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	110/5000 Deep learning algorithms will be explained theoretically and practical applications will be realized through projects.
Course Content	Regularization of deep or distributed models, optimization for training deep models, convolutional networks, sequence modeling: recurrent and recursive nets, structured probabilistic models for deep learning, linear factor models and auto-encoders
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.

COURSE LEARNING OUTCOMES

1	Expresses the arrangement of deep or distributed models.
2	Applies the deep learning methods with a project.
3	Optimizes for deep models.
4	Defines iterative and recursive networks.
5	Applies deep learning methods in real application areas.

COURSE'S CONTRIBUTION TO PROGRAM

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
Hours for off-the-classroom study (Pre-study, practice)	14	5	70
Assignments	5	8	40
Mid-terms	1	15	15
Final examination	1	28	28
Total Work Load ECTS Credit of the Course			195 7,5

COURSE DETAILS

Select Year

	Course Term	No	Instructors
Details	2025-2026 Spring	1	SERDAR İPLİKÇİ

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester	Mode of Delivery
CENG 588	DEEP LEARNING AND APPLICATIONS	3 + 0	1	Turkish	2025-2026 Spring	Face to Face

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Prof. Dr. SERDAR İPLİKÇİ	iplikci@pau.edu.tr		Course location is not specified.	%

Goals		110/5000 Deep learning algorithms will be explained theoretically and practical applications will be realized through projects.
Content		Regularization of deep or distributed models, optimization for training deep models, convolutional networks, sequence modeling: recurrent and recursive nets, structured probabilistic models for deep learning, linear factor models and auto-encoders

Topics

Materials

Materials are not specified.

Resources

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	50	Final Exam
Midterm Exam	50	Midterm Exam

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

Prospective Student