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 | Bachelor’s Degree (First Cycle) |
| 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 | Adequate knowledge in mathematics, science and computing fields; ability to apply theoretical and practical knowledge of these fields in solving engineering problems related to information systems. | |||||
| 2 | Ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose. | |||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose. | |||||
| 4 | Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in information systems engineering applications; ability to use information technologies effectively. | |||||
| 5 | Ability to gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the information systems discipline. | |||||
| 6 | Ability to work effectively in inter/inner disciplinary teams; ability to work individually. | |||||
| 7 | a. Effective oral and written communication skills in Turkish; ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. b. Knowledge of at least one foreign language; ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. | |||||
| 8 | Recognition of the need for lifelong learning; the ability to access information and follow recent developments in science and technology with continuous self-development. | |||||
| 9 | a. Ability to behave according to ethical principles, awareness of professional and ethical responsibility. b. Knowledge of the standards utilized in information systems engineering applications. | |||||
| 10 | a. Knowledge on business practices such as project management, risk management and change management. b. Awareness about entrepreneurship, and innovation. c. Knowledge on sustainable development. | |||||
| 11 | a. Knowledge of the effects of information systems engineering applications on the universal and social dimensions of health, environment, and safety. b. Awareness of the legal consequences of engineering solutions. | |||||
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 | ||