Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| INTRODUCTION to DEEP LEARNING | - | Spring Semester | 3+0 | 3 | 6 |
| Course Program |
| 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) | |
| 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 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | ||||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | ||||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | ||||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | ||||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | ||||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | ||||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | ||||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | ||||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | ||||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | ||||||
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 | - | Spring Semester | 3+0 | 3 | 6 |
| Course Program |
| 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) | |
| 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 | Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems. | ||||||
| 2 | Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | ||||||
| 3 | Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. | ||||||
| 4 | Ability to select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively. | ||||||
| 5 | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions. | ||||||
| 6 | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | ||||||
| 7 | Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions. | ||||||
| 8 | Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. | ||||||
| 9 | Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. | ||||||
| 10 | Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. | ||||||
| 11 | Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 30 | |
| Rate of Final Exam to Success | 70 | |
| Total | 100 | |