GRADUATE SCHOOL
M.SC. in Electrical and Electronics Engineering (Without Thesis)
EEE 517 | Course Introduction and Application Information
| Course Name |
Deep Learning Methods and Applications
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
EEE 517
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
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| Course Language |
English
|
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| Course Type |
Elective
|
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| Course Level |
Second / Third Cycle
|
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | Problem SolvingApplication: Experiment / Laboratory / WorkshopLecture / Presentation | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | This course aims to give students a general understanding of machine learning (ML) terminology; most common deep learning (DL) algorithms; applications of DL techniques to real-life problems with Python and TensorFlow (TF), parameter selections for learning and interpretation of the results. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This course is an introduction to deep learning which is behind many recent advances in AI, such as face recognition, self-driving cars, human-like speech generators, etc. A range of topics including basic neural networks, convolutional and recurrent network structures and NLP will be covered, focusing on both theory and practice. A strong mathematical background in calculus, linear algebra, probability & statistics, and coding experience with Python are expected. Machine learning background is good to have but not required. |
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|
Core Courses | |
| Major Area Courses | ||
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Machine Learning Basics | I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016. ISBN: 978-0262035613, Ch 5. |
| 2 | Introduction to Deep Learning | I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016. ISBN: 978-0262035613, Ch 6. |
| 3 | Introduction to TensorFlow | I. Zafar et al., Hands-On Convolutional Neural Networks with TensorFlow, Packt Publishing, 2018. ISBN: 978-1789130331, Ch 1. |
| 4 | Deep Neural Networks | I. Zafar et al., Hands-On Convolutional Neural Networks with TensorFlow, Packt Publishing, 2018. ISBN: 978-1789130331, Ch 2. |
| 5 | Foundations of Convolutional Neural Networks | I. Zafar et al., Hands-On Convolutional Neural Networks with TensorFlow, Packt Publishing, 2018. ISBN: 978-1789130331, Ch 2. |
| 6 | Homework 1 Presentations | |
| 7 | Image Classification with TensorFlow | I. Zafar et al., Hands-On Convolutional Neural Networks with TensorFlow, Packt Publishing, 2018. ISBN: 978-1789130331, Ch 3 |
| 8 | Object Detection and Segmentation with TensorFlow | I. Zafar et al., Hands-On Convolutional Neural Networks with TensorFlow, Packt Publishing, 2018. ISBN: 978-1789130331, Ch 4. |
| 9 | Improving Deep Neural Networks | I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016. ISBN: 978-0262035613, Ch 7, 8. |
| 10 | Homework 2 Presentations | |
| 11 | Recurrent Neural Networks | I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016. ISBN: 978-0262035613, Ch 10. |
| 12 | Natural Language Processing | I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016. ISBN: 978-0262035613, Ch 12. |
| 13 | Homework 3 Presentations | |
| 14 | Project Presentations | |
| 15 | Review of the Semester | |
| 16 | Final Exam |
| Course Notes/Textbooks |
|
| Suggested Readings/Materials |
|
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
3
|
30
|
| Presentation / Jury | ||
| Project |
1
|
40
|
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm | ||
| Final Exam |
1
|
30
|
| Total |
| Weighting of Semester Activities on the Final Grade |
4
|
70
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
30
|
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
14
|
4
|
56
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
3
|
12
|
36
|
| Presentation / Jury |
0
|
||
| Project |
1
|
60
|
60
|
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
0
|
||
| Final Exam |
1
|
20
|
20
|
| Total |
220
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | Accesses information in breadth and depth by conducting scientific research in Electrical and Electronics Engineering, evaluates, interprets and applies information. |
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| 2 | Is well-informed about contemporary techniques and methods used in Electrical and Electronics Engineering and their limitations. |
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| 3 | Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines. |
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| 4 | Is informed about new and upcoming applications in the field and learns them whenever necessary. |
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| 5 | Defines and formulates problems related to Electrical and Electronics Engineering, develops methods to solve them and uses progressive methods in solutions. |
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| 6 | Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs. |
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| 7 | Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process. |
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| 8 | Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility. |
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| 9 | Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale. |
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| 10 | Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form. |
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| 11 | Is knowledgeable about the social, environmental, health, security and law implications of Electrical and Electronics engineering applications, knows their project management and business applications, and is aware of their limitations in Electrical and Electronics engineering applications. |
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| 12 | Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest