Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN420Introduction to Deep Learning3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Being an introduction to the common terminology and basic applications of deep learning, the aim of this course is to let students gain knowledge of fundamental deep learning techniques and apply them to basic real-world problems.
Course Content Neural networks, learning in neural networks, backpropagation,supervised deep learning techniques, convolutional neural networks, unsupervised deep learning methods, recurrent neural networks, foundations of deep reinforcement learning, modelling sequential data, foundations of deep generative adversarial networks.
Course Methods and Techniques Lecture, Drill and Practice, Problem Solving
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 ) and ( AIN422 )
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Cemil Zalluhoğlu
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Ian Goodfellow, Yoshua Bengio, Aaron Courville "Deep Learning" MIT Press, 2016. Michael Nielsen, ?Neural Networks and Deep Learning?, Online Book, 2016
Course Notes Ian Goodfellow, Yoshua Bengio, Aaron Courville "Deep Learning" MIT Press, 2016.

Michael Nielsen, “Neural Networks and Deep Learning”, Online Book, 2016


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 1 % 24
Practice 4 % 8
Project 1 % 28
Final examination 1 % 40
Total
7
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 10 2 20
Practice 4 1 4
Project 1 70 70
Preparation for Midterm Exam 1 15 15
General Exam Preparation 1 25 25
Total Work Load   Number of ECTS Credits 5,86666666666667 176

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 In completition of this course, the student will 1- Have a general knowledge about deep learning techniques and problems.
2 2- Provide basic solutions to various deep learning problems.
3 3- Have the necessary background to start conducting research in the area.
4 4- Have knowledge about the potential application areas of deep learning.
5 5- Have project experience on deep learning.
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction
2 Neural Networks and Learning in NNs
3 Backpropagation and analysis of different loss functions
4 Convolutional Neural Networks
5 Convolutional Neural Networks
6 Recurrent neural networks and modelling sequence data
7 Long short term memory and GRU models
8 Midterm
9 Attention models
10 Autoencoders and unsupervised deep learning
11 Representation learning
12 Deep Generative Adversarial Networks
13 Foundations of Deep Reinforcement Learning
14 Applications of deep learning
15 Final exam preparation
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 3 4 4 4 4 4 2 3 2 5
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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