ECTS - Advanced Deep Learning Techniques and Applications

Atılım University - ECTS INFORMATION GUIDE

Advanced Deep Learning Techniques and Applications (CMPE452) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Advanced Deep Learning Techniques and Applications CMPE452 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Elective Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery
Learning and Teaching Strategies Lecture, Question and Answer, Drill and Practice, Problem Solving.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives The course objective is to equip students with a good understanding of deep learning principles, enabling them to design, implement, and evaluate advanced neural network models for various real-world applications.
Course Learning Outcomes The students who succeeded in this course;
  • Grasp advanced concepts of artificial neural networks, including hands-on experience with Python programming language.
  • Develop proficiency in deep supervised learning techniques and backpropagation algorithms, applying them to datasets from platforms like Kaggle.
  • Understand and implement convolutional neural networks for tasks such as object recognition, image segmentation, and vision-based navigation.
  • Explore structural prediction methods and natural language processing applications within deep learning frameworks.
  • Apply optimization techniques to enhance AI model performance, reduce overfitting, and effectively initialize neural networks.
  • Investigate the utilization of deep learning in specialized domains and assess current challenges and future trends in the field.
Course Content Artificial intelligence, machine learning and deep learning, mathematical building blocks of neural networks, supervised learning, backpropagation, CNNs, object recognition, image segmentation, feature extraction, NLP, optimization techniques.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Course Introduction, Introduction to Machine Learning Course Book – Ch. 1.1
2 Introduction to Neural Networks, Coding in Python (Artificial Brain Development) Course Book – Ch. 2.1
3 Introduction to Deep Learning Course Book – Ch. 1.2, Ch. 1.3
4 Deep Supervised Learning I, Deep Supervised Learning II Lecture Notes
5 Part I: Backpropagation Part II: Experimenting on a Deep Learning Model via a Kaggle Dataset Course Book – Ch. 2.2, Ch. 4.1, Ch. 4.2, Ch. 5.1
6 Technical Progress of Convolutional Networks, Convolutional Networks for; Multiple Object Recognition, Visual Object Detection, and Simple Object Recognition Course Book – Ch. 3.1
7 Midterm Exam
8 ConvNet for Segmentation and Vision-Based Navigation, Convolutional Networks in Image Segmentation and Scene Labeling, Convolutional Networks for Real Object Recognition Course Book – Ch. 3.1
9 ConvNets as Generic Feature Extractors, Image Similarity Matching with Siamese Networks Embedding, Accurate Depth Estimation from Stereo, Body Pose Estimation, Vision Project Ideas, Examples of Deep Learning and Convolutional Networks in Speech, Audio, and Signals, Software Tools and Hardware Acceleration for Convolutional Networks Course Book – Ch. 3.1, Ch. 3.3
10 Structural Prediction and Natural Language Processing Course Book – Ch. 8.1, Ch. 8.3
11 Part I: More Backpropagation Part II: Semi-supervised Image Recognition Course Book – Ch. 6.1, Ch. 6.2
12 Techniques (Optimization, Reducing Overfitting, Initialization) Course Book – Ch. 5.3, Ch. 9.1, Ch. 9.2
13 Coding in Python (Image Segmentation) Deep Learning with Python, Second Edition by Francois Chollet – Ch. 9.2
14 Disaster Risk Monitoring Using Satellite Imagery Course Book – Ch. 10.2, Ch. 10.3
15 Review
16 Final Exam

Sources

Course Book 1. Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow by TransformaTech Institute, independently published Nov. 10, 2024.
Other Sources 2. NVIDIA Deep Learning Institute: https://www.nvidia.com/en-us/training/
3. Deep Learning with Python, Second Edition by Francois Chollet, Publisher: Manning, Dec. 21, 2021.
4. Deep Learning by Ian Goodfellow, Publisher: The MIT Press, Nov. 18, 2016.
5. Neural Networks and Deep Learning: A Textbook by Charu C. Aggarwal, Publisher: Springer, Sep. 13, 2018.
6. PyTorch web page: https://pytorch.org/ & TensorFlow web page: https://www.tensorflow.org/

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 1 20
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 35
Final Exam/Final Jury 1 45
Toplam 3 100
Percentage of Semester Work 55
Percentage of Final Work 45
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
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.
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.
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.
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.
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.
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 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project
Report
Homework Assignments 1 18 18
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 12 12
Prepration of Final Exams/Final Jury 1 15 15
Total Workload 125