ECTS - Advanced Deep Learning Techniques and Applications
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 | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | |
| Learning and Teaching Strategies | Lecture, Question and Answer, Drill and Practice, Problem Solving. |
| Course Lecturer(s) |
|
| 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;
|
| 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 | An ability to apply advanced knowledge of computing and/or informatics to solve software engineering problems. | |||||
| 2 | Develop solutions using different technologies, software architectures and life-cycle approaches. | |||||
| 3 | An ability to design, implement and evaluate a software system, component, process or program by using modern techniques and engineering tools required for software engineering practices. | |||||
| 4 | An ability to gather/acquire, analyze, interpret data and make decisions to understand software requirements. | |||||
| 5 | Skills of effective oral and written communication and critical thinking about a wide range of issues arising in the context of working constructively on software projects. | |||||
| 6 | An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain. | |||||
| 7 | An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering. | |||||
| 8 | Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies. | |||||
| 9 | An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions. | |||||
| 10 | Promote the development, adoption and sustained use of standards of excellence for software engineering practices. | |||||
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 | ||