Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
7EEE 417Deep Learning3+0+035

Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program Electrical and Electronics Engineering
Mode of Delivery Face to Face
Type of Course Unit Elective
Objectives of the Course Deep learning, a branch of machine learning, allows computers to model high-level abstractions from experience (encoded in large-scale labeled and unlabeled data). Recent advances in computing hardware and algorithms have made it a popular tool for artificial intelligence. This course aims at clarifying the theory behind deep learning methods while providing the students with the skills of their effective use in many domains such as computer vision and natural language processing.
Course Content Machine Learning Fundamentals, Deep Learning Tools - Caffe, Torch, TensorFlow, Theano, Feedforward Deep Networks, Regularization of Deep or Distributed Models, Optimization for Training Deep Models, Convolutional Networks, Sequence Modeling: Recurrent and Recursive Nets, Structured Probabilistic Models for Deep Learning , Linear Factor Models and Auto-Encoders, Computer Vision Applications, Big Data Applications, Natural Language Processing Applications, Speech Processing Applications
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Prof.Dr. Hüseyin GÖKSU
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Deep Learning by Yoshua Bengio et al MIT Press, 2015

Course Category
Mathematics and Basic Sciences %10
Engineering %60
Engineering Design %30

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Assignment 1 % 30
Final examination 1 % 40
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Assignments 3 9 27
Mid-terms 1 2 2
Final examination 1 2 2
Total Work Load   Number of ECTS Credits 4 115

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Learn the basic methods of machine learning
2 Learn the basics of deep learning
3 Applying deep learning to classification and regression problems
4 Learn how to apply deep learning algorithms to complex engineering problems


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 What is Deep Learning . .
2 The Mathematical Building Blocks of Neural Networks . .
3 Getting Started with Neural Networks . .
4 Fundamentals of Machine Learning . .
5 Deep Learning for Computer Vision – Part 1 . .
6 Deep Learning for Computer Vision – Part 2 . .
7 Deep Learning for Computer Vision – Part 3 . .
8 Deep Learning for Text and Sequences – Part 1 . .
9 Deep Learning for Text and Sequences – Part 2 . .
10 Deep Learning for Text and Sequences – Part 3 . .
11 Advanced Deep Learning Best Practices . .
12 Generative Deep Learning – Part 1 . .
13 Generative Deep Learning – Part 2 . .
14 Generative Deep Learning – Part 3 . .


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
All 3 3 3 3 3 2 2 2 2 2 2
C1 3 3 3 3 3 2 2 2 2 2 2
C2 3 3 3 3 3 2 2 2 2 2 2
C3 4 4 4 4 4 2 2 2 2 2 2
C4 5 5 5 5 5 2 2 2 2 2 2

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https://obs.akdeniz.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2429255&lang=en