PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
CENG 588	DEEP LEARNING AND APPLICATIONS	3 + 0	2nd 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.
Mode of Delivery	Face to Face

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

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

Prospective Student