2023 Faculty Courses School of Engineering Department of Systems and Control Engineering Graduate major in Systems and Control Engineering
Image Recognition
- Academic unit or major
- Graduate major in Systems and Control Engineering
- Instructor(s)
- Masayuki Tanaka
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Fri (S6-219(S621))
- Class
- -
- Course Code
- SCE.I501
- Number of credits
- 100
- Course offered
- 2023
- Offered quarter
- 2Q
- Syllabus updated
- Jul 8, 2025
- Language
- English
Syllabus
Course overview and goals
Machine learning is widely used in many applications including autonomous vehicles, robotics, and medical diagnosis. Recognition of an image is one of the best examples of machine learning or artificial intelligence. Topics of the image recognition course includes fundamental components of deep learning such as convolution layer, full connection layer, pooling layer, ReLU layer, and a softmax layer. In this course, students develop and train their network with matlab by themselves.
Course description and aims
Students are expected to
(i) gain an ability to build and learn deep neural networks,
(ii) gain an ability to use numerical computing environments using MATLAB to solve engineering problems,
(iii) gain practical skill to apply the deep learning techniques such as momentum, data arugumentation and filter setting, after taking this course.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
A faculty who has a private company experience gives a lecture.
Keywords
Object recognition, Convolutional neural network (CNN), Deep learning, matlab
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
This class is a kind of active learning. Instructor will give some information, but students are required to develop their matlab code.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction | Introduction |
| Class 2 | Example of image classification | Example of image classification |
| Class 3 | Gradient decent approach | Gradient decent approach |
| Class 4 | Loss function | Loss function |
| Class 5 | Overfitting | Overfitting |
| Class 6 | eature extraction and transfer learning | eature extraction and transfer learning |
| Class 7 | Classification methods | Classification methods |
| Class 8 | Applications | Applications |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None
Reference books, course materials, etc.
None
Evaluation methods and criteria
Assignments and report
Related courses
- SCE.I531 ： Computer Vision
Prerequisites
None
Other
Students need to implement the matlab code by themselves.
Student who took the course of Computational Imaging (#SCE.I501) cannot take this course.