Information Package / Course Catalogue
Introduction to Deep Learning
Course Code: CSE439
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: English
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

Introducing the fundamentals of deep learning, deep learning concepts, and applications. Enabling students to develop deep learning projects with Python.

Course Content

The mathematical foundations of deep learning, artificial neural networks, neural network training, convolutional neural networks, recurrent neural networks, long short-term memory, non-linear activation functions, deep learning algorithms and applications

Name of Lecturer(s)
Learning Outcomes
1.Understanding the fundamentals of deep learning
2.Understanding how basic deep learning models such as artificial neural networks, convolutional neural networks, and recurrent neural networks work.
3.Applying deep learning algorithms and techniques
4.Become more interested in developing new deep learning for solving different types of problems
5.Developing deep learning projects
Recommended or Required Reading
1.François Chollet, “Deep Learning with Python” , 2 nd Ed., Manning, 2021.
2.Aurélien Géron, “Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems”, 2 nd Ed., O’Reilly, 2019.
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
Introduction to Deep Learning
Week 2 - Theoretical & Practice
Core Concepts about Deep Learning
Week 3 - Theoretical & Practice
Mathematical Building Blocks of Artificial Neural Networks
Week 4 - Theoretical & Practice
Classification via Artificial Neural Networks
Week 5 - Theoretical & Practice
Regression via Artificial Neural Networks
Week 6 - Theoretical & Practice
Overfitting, Underfitting
Week 7 - Theoretical & Practice
Overfitting, Underfitting
Week 8 - Theoretical & Practice
Training Deep Neural Networks
Week 9 - Theoretical & Practice
Deep Learning for Computer Vision
Week 10 - Theoretical & Practice
Deep Learning for Textual Data
Week 11 - Theoretical & Practice
Deep Learning for Time Series
Week 12 - Theoretical & Practice
Encoder-Decoder Models
Week 13 - Theoretical & Practice
Attention Mechanism
Week 14 - Theoretical & Practice
Transformer Models
Assessment Methods and Criteria
Type of AssessmentCountPercent
Project2%100
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory141242
Lecture - Practice140228
Assignment140342
Project281036
TOTAL WORKLOAD (hours)148
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
5
5
5
4
OÇ-2
5
5
4
5
4
OÇ-3
5
5
4
4
5
4
OÇ-4
4
4
4
4
OÇ-5
5
5
5
4
5
4
Adnan Menderes University - Information Package / Course Catalogue
2026