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
-
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)
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
