Information Package / Course Catalogue
Advanced Deep Learning
Course Code: MCS523
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: English
Work Placement: N/A
Theory: 3
Prt.: 0
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 work.
3.Applying deep learning algorithms and techniques
4.Developing deep learning projects
5.Understanding how basic deep learning models such as convolutional neural networks, and recurrent neural networks work.
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
Introduction to Deep Learning
Week 2 - Theoretical
Core Concepts about Deep Learning
Week 3 - Theoretical
Mathematical Building Blocks of Artificial Neural Networks
Week 4 - Theoretical
Classification via Artificial Neural Networks
Week 5 - Theoretical
Regression via Artificial Neural Networks
Week 6 - Theoretical
Overfitting, Underfitting
Week 7 - Theoretical
Training Deep Neural Networks
Week 8 - Theoretical
Deep Learning for Computer Vision
Week 9 - Theoretical
Deep Learning for Textual Data
Week 10 - Theoretical
Deep Learning for Time Series
Week 11 - Theoretical
Encoder-Decoder Models
Week 12 - Theoretical
Attention Mechanism
Week 13 - Theoretical
Transformer Models
Week 14 - Theoretical
Transformer Models
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Presentation1%10
Midterm Examination1%15
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory143384
Presentation 1516
Individual Work140114
Midterm Examination120222
Final Examination120323
TOTAL WORKLOAD (hours)149
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
OÇ-1
3
3
3
3
4
3
4
3
3
OÇ-2
4
4
4
3
5
4
3
4
3
OÇ-3
3
3
5
5
5
4
3
5
5
OÇ-4
5
4
5
4
4
4
5
5
4
OÇ-5
3
3
3
3
4
3
4
3
3
Adnan Menderes University - Information Package / Course Catalogue
2026