DEEP LEARNING

SEÇ166

Course Code: SEÇ166
Semester: -
Theory+Practice: 3 + 0
ECTS: 5

Course Type

Elective Course

Course Level

Associate Degree

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Gaining the ability to understand and apply the fundamentals of artificial neural networks.
Understanding deep learning models such as convolutional and recurrent neural networks.
Learn techniques for training and evaluating deep learning models.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

Course Description

Deep Learning course aims to teach the fundamentals of artificial neural networks and deep learning techniques and to provide students with the skills to develop applied deep learning models.

Course Contents

WEEK	TOPICS
1^st Week	Introduction and Basic Concepts
2^nd Week	Artificial Neural Networks and Back Propagation Algorithm
3^rd Week	Convolutional Neural Network
4^th Week	Deep Natural Language Processing
5^th Week	Recurrent Neural Networks
6^th Week	Training and Evaluation of Deep Learning Models
7^th Week	Activation Functions and Loss Functions
8^th Week	Midterm exam
9^th Week	Deep Learning Applications: Image Classification
10^th Week	Deep Learning Applications: Object Recognition
11^th Week	Deep Learning Applications: Sentiment Analysis and Text Classification
12^th Week	Deep Learning Applications: Voice Recognition
13^th Week	Transfer Learning and Model Improvement
14^th Week	Deep Learning and Ethical Issues

Recommended or Required Reading

Planned Learning Activities and Teaching Methods

Lecture
Practice
Questions/Answers

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	40
Total(%)		40
Contribution of In-term Studies to Overall Grade(%)		40
Contribution of Final Examination to Overall Grade(%)		60
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	1	1	1
Preparation for Quiz
Individual or group work	14	3	42
Preparation for Final exam	14	2	28
Course hours	14	3	42
Preparation for Midterm exam	14	2	28
Laboratory (including preparation)
Final exam	1	1	1
Homework
Total Work Load			142
Total Work Load / 30 (h)			4.73
ECTS Credit of the Course			5

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3
K1	X	X	X
K2
K3	X	X	X
K4
K5	X	X	X
K6
K7
K8	X	X	X
K9
K10
K11	X	X	X
K12
K13