Syllabus Application
CS 515
Deep Learning
Faculty
Faculty of Engineering and Natural Sciences
Semester
Spring 2025-2026
Course
CS 515 -
Deep Learning
Time/Place
Time
Week Day
Place
Date
13:40-14:30
Mon
FENS-L063
Feb 16-May 22, 2026
13:40-15:30
Thu
FENS-G029
Feb 16-May 22, 2026
Level of course
Masters
Course Credits
SU Credit:3, ECTS:10
Prerequisites
( CS 512 or CS 512 or EE 566 or EE 566)
Corequisites
-
Course Type
Lecture
Instructor(s) Information
Mehmet Emre Özfatura
Course Information
Catalog Course Description
This course covers the theory and foundations of Artificial Neural Networks (ANN) and various ANN architectures, such as the single and multi- layer perceptrons, Hopfield and Kohonen networks, and deep learning architectures (convolutional neural networks, autoencoders, restricted Boltzman machines, recurrent networks and LSTMs, and generative adversarial networks). Students will be expected to develop systems for machine learning problems from the computer vision and natural language understanding areas.
Course Learning Outcomes:
Course Objective
The objective of this course is to introduce students to deep learning.
Sustainable Development Goals (SDGs) Related to This Course:
| Industry, Innovation and Infrastructure |