Fundamentals of Deep Learning (CMPE430) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Fundamentals of Deep Learning	CMPE430	Area Elective	2	2	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Technical Elective Courses
Course Level	Bachelor’s Degree (First Cycle)
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives	The course objective is to provide an introduction to Deep Neural Network architectures, learning algorithms, and their applications.
Course Learning Outcomes	The students who succeeded in this course; Describe the concepts and techniques of Deep Neural Networks Reason about the behavior of Deep Neural Networks Evaluate which Deep Neural Network model is appropriate to a particular application Evaluate Deep Neural Network models Apply Deep Neural Networks to particular applications Identify steps to develop Deep Neural Networks
Course Content	Artificial intelligence, machine learning, and deep learning, mathematical building blocks of neural networks, binary classification, multiclass classification, regression, deep learning for computer vision.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Artificial intelligence, machine learning, and deep learning	Ch. 1.1
2	Probabilistic modeling, Decision trees, Random forests, Neural networks	Ch. 1.2, Ch. 1.3
3	Data representations for neural networks	Ch. 2.1, Ch. 2.2
4	Tensor operations I	Ch. 2.3
5	Tensor operations II	Ch. 2.3
6	Gradient-based optimization I	Ch. 2.4
7	Gradient-based optimization II	Ch. 2.5
8	Deep Neural Network Model, Layers, Loss Functions	Ch. 3.1, Ch. 3.2, Ch. 3.3
9	Binary classification I	Ch. 3.4
10	Binary classification II	Ch. 3.4
11	Multiclass classification	Ch. 3.5
12	Regression	Ch. 3.6
13	Model Evaluating, Data preprocessing	Ch. 4.1, Ch. 4.2, Ch. 4.3
14	Overfitting and Underfitting, Universal workflow	Ch. 4.4, Ch. 4.5

Sources

Course Book	1. Deep Learning with Python Sep 11, 2018 by Francois Chollet , Mark Thomas , Manning Publications Co.
Other Sources	2. Deep Learning, Goodfellow, Ian, Publisher: Mit Press Place of Publication: Cambridge, Pub Year:2017
	3. Tensorflow web page, https://www.tensorflow.org
	4. Deep Learning : Fundamentals, Methods and Applications, Porter, Julius, Nova Science Publishers, Inc. 2016

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	1	30
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	1	10
Presentation	-	-
Project	-	-
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	30
Final Exam/Final Jury	1	30
Toplam	4	100

Percentage of Semester Work	70
Percentage of Final Work	30
Total	100

Course Category

Core Courses	X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Gains adequate knowledge in mathematics, science, and subjects specific to the software engineering discipline; acquires the ability to apply theoretical and practical knowledge of these areas to complex engineering problems.
2	Gains the ability to identify, define, formulate, and solve complex engineering problems; selects and applies proper analysis and modeling techniques for this purpose.					X
3	Develops the ability to design a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose.					X
4	Demonstrates the ability to select, and utilize modern techniques and tools essential for the analysis and determination of complex problems in software engineering applications; uses information technologies effectively.					X
5	Develops the ability to design experiments, gather data, analyze, and interpret results for the investigation of complex engineering problems or research topics specific to the software engineering discipline.					X
6	Demonstrates the ability to work effectively both individually and in disciplinary and interdisciplinary teams in fields related to software engineering.
7	Demonstrates the ability to communicate effectively in Turkish, both orally and in writing; to write effective reports and understand written reports, to prepare design and production reports, to deliver effective presentations, and to give and receive clear and understandable instructions.
8	Gains knowledge of at least one foreign language; acquires the ability to write effective reports and understand written reports, prepare design and production reports, deliver effective presentations, and give and receive clear and understandable instructions.
9	Acquires an awareness of the necessity of lifelong learning; the ability to access information, follow developments in science and technology, and continuously improve oneself.
10	Acts in accordance with ethical principles and possesses knowledge of professional and ethical responsibilities.
11	Knows the standards used in software engineering practices.
12	Knows about business practices such as project management, risk management and change management.
13	Gains awareness about entrepreneurship and innovation.
14	Gains knowledge on sustainable development.
15	Has knowledge about the universal and societal impacts of software engineering practices on health, environment, and safety, as well as the contemporary issues reflected in the field of engineering.
16	Acquires awareness of the legal consequences of engineering solutions.
17	Applies knowledge and skills in identifying user needs, developing user-focused solutions and improving user experience.			X
18	Gains the ability to apply engineering approaches in the development of software systems by carrying out analysis, design, implementation, verification, validation, and maintenance processes.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	2	32
Laboratory	12	2	24
Application
Special Course Internship
Field Work
Study Hours Out of Class	16	2	32
Presentation/Seminar Prepration
Project
Report
Homework Assignments	1	10	10
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	1	12	12
Prepration of Final Exams/Final Jury	1	15	15
Total Workload			125