Course Detail
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
INTRODUCTION to DEEP LEARNING | COE3268010 | Spring Semester | 3+0 | 3 | 6 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | English |
Course Level | First Cycle (Bachelor's Degree) |
Course Type | Elective |
Course Coordinator | Prof.Dr. Bahadır Kürşat GÜNTÜRK |
Name of Lecturer(s) | Prof.Dr. Bahadır Kürşat GÜNTÜRK |
Assistant(s) | |
Aim | This course is an introduction to deep learning, a branch of machine learning concerned with the development and application of modern neural networks. We will cover a range of topics from basic neural networks, convolutional and recurrent network structures, deep unsupervised learning, and applications to problem domains like computer vision, image processing and natural language processing. The course will introduce training and optimization strategies in deep networks both for supervised and unsupervised learning tasks. |
Course Content | This course contains; Introduction to Machine Learning and Neural Networks,Training Neural Networks,Convolutional Neural Networks (CNNs) ,Network Layers in CNNs ,Deep Learning Hardware and Software,Deep Network Architectures,Deep Learning Strategies,Computer vision applications,Computer Vision and Deep Learning ,Image processing and Deep Learning,Natural Language Processing with Deep Learning,Recurrent Neural Networks and LSTMs,Unsupervised Learning and Generative Modeling,Advanced Applications of Deep Learning . |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
Design convolutional neural networks for supervised/unsupervised learning | 2, 21, 6, 9 | A, E, F |
Analyze the effects of hyper-parameters on learning performance | 12, 2, 21, 6, 9 | A, E, F |
Apply learning techniques for training deep networks | 12, 2, 21, 6, 9 | A, E, F |
Recognize the applications of deep networks in computer vision, image processing and natural language processing | 2, 21, 6, 9 | E, F |
Use current software and hardware tools for deep learning | 2, 21, 6, 9 | E, F |
Teaching Methods: | 12: Problem Solving Method, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Introduction to Machine Learning and Neural Networks | |
2 | Training Neural Networks | |
3 | Convolutional Neural Networks (CNNs) | |
4 | Network Layers in CNNs | |
5 | Deep Learning Hardware and Software | |
6 | Deep Network Architectures | |
7 | Deep Learning Strategies | |
8 | Computer vision applications | |
9 | Computer Vision and Deep Learning | |
10 | Image processing and Deep Learning | |
11 | Natural Language Processing with Deep Learning | |
12 | Recurrent Neural Networks and LSTMs | |
13 | Unsupervised Learning and Generative Modeling | |
14 | Advanced Applications of Deep Learning | |
Resources |
Deep Learning, I. Goodfellow, Y. Bengio and A. Courville , MIT Press, http://www.deeplearningbook.org , 2016. |
Machine Learning Yearning, Andrew Ng, http://www.mlyearning.org/,
Intel® AI Academy Deep Learning 501
https://software.intel.com/en-us/ai-academy/students/kits/deep-learning-501
|
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | 1. An ability to apply knowledge of mathematics, science, and engineering | X | | | | |
2 | 2. An ability to identify, formulate, and solve engineering problems | | | | X | |
3 | 3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | | | | | X |
4 | 4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | | | | | X |
5 | 5. An ability to design and conduct experiments, as well as to analyze and interpret data | | | | | |
6 | 6. An ability to function on multidisciplinary teams | | | | X | |
7 | 7. An ability to communicate effectively | | | X | | |
8 | 8. A recognition of the need for, and an ability to engage in life-long learning | | | X | | |
9 | 9. An understanding of professional and ethical responsibility | | | | | |
10 | 10. A knowledge of contemporary issues | | | | | |
11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | | | | | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 30 |
Rate of Final Exam to Success | | 70 |
Total | | 100 |
ECTS / Workload Table |
Activities | Number of | Duration(Hour) | Total Workload(Hour) |
Course Hours | 14 | 3 | 42 |
Guided Problem Solving | 0 | 0 | 0 |
Resolution of Homework Problems and Submission as a Report | 5 | 12 | 60 |
Term Project | 14 | 2 | 28 |
Presentation of Project / Seminar | 0 | 0 | 0 |
Quiz | 0 | 0 | 0 |
Midterm Exam | 1 | 20 | 20 |
General Exam | 1 | 30 | 30 |
Performance Task, Maintenance Plan | 0 | 0 | 0 |
Total Workload(Hour) | 180 |
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(180/30) | 6 |
ECTS of the course: 30 hours of work is counted as 1 ECTS credit. |
Detail Informations of the Course
Course Description
Course | Code | Semester | T+P (Hour) | Credit | ECTS |
---|
INTRODUCTION to DEEP LEARNING | COE3268010 | Spring Semester | 3+0 | 3 | 6 |
Prerequisites Courses | |
Recommended Elective Courses | |
Language of Course | English |
Course Level | First Cycle (Bachelor's Degree) |
Course Type | Elective |
Course Coordinator | Prof.Dr. Bahadır Kürşat GÜNTÜRK |
Name of Lecturer(s) | Prof.Dr. Bahadır Kürşat GÜNTÜRK |
Assistant(s) | |
Aim | This course is an introduction to deep learning, a branch of machine learning concerned with the development and application of modern neural networks. We will cover a range of topics from basic neural networks, convolutional and recurrent network structures, deep unsupervised learning, and applications to problem domains like computer vision, image processing and natural language processing. The course will introduce training and optimization strategies in deep networks both for supervised and unsupervised learning tasks. |
Course Content | This course contains; Introduction to Machine Learning and Neural Networks,Training Neural Networks,Convolutional Neural Networks (CNNs) ,Network Layers in CNNs ,Deep Learning Hardware and Software,Deep Network Architectures,Deep Learning Strategies,Computer vision applications,Computer Vision and Deep Learning ,Image processing and Deep Learning,Natural Language Processing with Deep Learning,Recurrent Neural Networks and LSTMs,Unsupervised Learning and Generative Modeling,Advanced Applications of Deep Learning . |
Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
Design convolutional neural networks for supervised/unsupervised learning | 2, 21, 6, 9 | A, E, F |
Analyze the effects of hyper-parameters on learning performance | 12, 2, 21, 6, 9 | A, E, F |
Apply learning techniques for training deep networks | 12, 2, 21, 6, 9 | A, E, F |
Recognize the applications of deep networks in computer vision, image processing and natural language processing | 2, 21, 6, 9 | E, F |
Use current software and hardware tools for deep learning | 2, 21, 6, 9 | E, F |
Teaching Methods: | 12: Problem Solving Method, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method |
Assessment Methods: | A: Traditional Written Exam, E: Homework, F: Project Task |
Course Outline
Order | Subjects | Preliminary Work |
---|
1 | Introduction to Machine Learning and Neural Networks | |
2 | Training Neural Networks | |
3 | Convolutional Neural Networks (CNNs) | |
4 | Network Layers in CNNs | |
5 | Deep Learning Hardware and Software | |
6 | Deep Network Architectures | |
7 | Deep Learning Strategies | |
8 | Computer vision applications | |
9 | Computer Vision and Deep Learning | |
10 | Image processing and Deep Learning | |
11 | Natural Language Processing with Deep Learning | |
12 | Recurrent Neural Networks and LSTMs | |
13 | Unsupervised Learning and Generative Modeling | |
14 | Advanced Applications of Deep Learning | |
Resources |
Deep Learning, I. Goodfellow, Y. Bengio and A. Courville , MIT Press, http://www.deeplearningbook.org , 2016. |
Machine Learning Yearning, Andrew Ng, http://www.mlyearning.org/,
Intel® AI Academy Deep Learning 501
https://software.intel.com/en-us/ai-academy/students/kits/deep-learning-501
|
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications |
No | Program Qualification | Contribution Level |
1 | 2 | 3 | 4 | 5 |
1 | 1. An ability to apply knowledge of mathematics, science, and engineering | X | | | | |
2 | 2. An ability to identify, formulate, and solve engineering problems | | | | X | |
3 | 3. An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | | | | | X |
4 | 4. An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | | | | | X |
5 | 5. An ability to design and conduct experiments, as well as to analyze and interpret data | | | | | |
6 | 6. An ability to function on multidisciplinary teams | | | | X | |
7 | 7. An ability to communicate effectively | | | X | | |
8 | 8. A recognition of the need for, and an ability to engage in life-long learning | | | X | | |
9 | 9. An understanding of professional and ethical responsibility | | | | | |
10 | 10. A knowledge of contemporary issues | | | | | |
11 | 11. The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | | | | | |
Assessment Methods
Contribution Level | Absolute Evaluation |
Rate of Midterm Exam to Success | | 30 |
Rate of Final Exam to Success | | 70 |
Total | | 100 |
Numerical Data
Ekleme Tarihi: 09/10/2023 - 10:50Son Güncelleme Tarihi: 09/10/2023 - 10:51
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